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Annual Meeting of the Association for Computational Linguistics 1

https://aclanthology.org/2023.semeval-1.255.bib

https://aclanthology.org/2023.semeval-1.255/

@inproceedings{hu-etal-2023-ucas, title = "{UCAS}-{IIE}-{NLP} at {S}em{E}val-2023 Task 12: Enhancing Generalization of Multilingual {BERT} for Low-resource Sentiment Analysis", author = "Hu, Dou and Wei, Lingwei and Liu, Yaxin and Zhou, Wei and Hu, Songlin", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.255", doi = "10.18653/v1/2023.semeval-1.255", pages = "1849--1857", abstract = "This paper describes our system designed for SemEval-2023 Task 12: Sentiment analysis for African languages. The challenge faced by this task is the scarcity of labeled data and linguistic resources in low-resource settings. To alleviate these, we propose a generalized multilingual system SACL-XLMR for sentiment analysis on low-resource languages. Specifically, we design a lexicon-based multilingual BERT to facilitate language adaptation and sentiment-aware representation learning. Besides, we apply a supervised adversarial contrastive learning technique to learn sentiment-spread structured representations and enhance model generalization. Our system achieved competitive results, largely outperforming baselines on both multilingual and zero-shot sentiment classification subtasks. Notably, the system obtained the 1st rank on the zero-shot classification subtask in the official ranking. Extensive experiments demonstrate the effectiveness of our system.", }

This paper describes our system designed for SemEval-2023 Task 12: Sentiment analysis for African languages. The challenge faced by this task is the scarcity of labeled data and linguistic resources in low-resource settings. To alleviate these, we propose a generalized multilingual system SACL-XLMR for sentiment analysis on low-resource languages. Specifically, we design a lexicon-based multilingual BERT to facilitate language adaptation and sentiment-aware representation learning. Besides, we apply a supervised adversarial contrastive learning technique to learn sentiment-spread structured representations and enhance model generalization. Our system achieved competitive results, largely outperforming baselines on both multilingual and zero-shot sentiment classification subtasks. Notably, the system obtained the 1st rank on the zero-shot classification subtask in the official ranking. Extensive experiments demonstrate the effectiveness of our system.

UCAS-IIE-NLP at SemEval-2023 Task 12: Enhancing Generalization of Multilingual BERT for Low-resource Sentiment Analysis

semeval-1.255

Poster

2306.01093

https://aclanthology.org/2023.semeval-1.256.bib

https://aclanthology.org/2023.semeval-1.256/

@inproceedings{gupta-etal-2023-steno, title = "Steno {AI} at {S}em{E}val-2023 Task 6: Rhetorical Role Labelling of Legal Documents using Transformers and Graph Neural Networks", author = "Gupta, Anshika and Furniturewala, Shaz and Kumari, Vijay and Sharma, Yashvardhan", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.256", doi = "10.18653/v1/2023.semeval-1.256", pages = "1858--1862", abstract = "A legal document is usually long and dense requiring human effort to parse it. It also contains significant amounts of jargon which make deriving insights from it using existing models a poor approach. This paper presents the approaches undertaken to perform the task of rhetorical role labelling on Indian Court Judgements. We experiment with graph based approaches like Graph Convolutional Networks and Label Propagation Algorithm, and transformer-based approaches including variants of BERT to improve accuracy scores on text classification of complex legal documents.", }

A legal document is usually long and dense requiring human effort to parse it. It also contains significant amounts of jargon which make deriving insights from it using existing models a poor approach. This paper presents the approaches undertaken to perform the task of rhetorical role labelling on Indian Court Judgements. We experiment with graph based approaches like Graph Convolutional Networks and Label Propagation Algorithm, and transformer-based approaches including variants of BERT to improve accuracy scores on text classification of complex legal documents.

Steno AI at SemEval-2023 Task 6: Rhetorical Role Labelling of Legal Documents using Transformers and Graph Neural Networks

semeval-1.256

Poster

https://aclanthology.org/2023.semeval-1.257.bib

https://aclanthology.org/2023.semeval-1.257/

@inproceedings{vladika-matthes-2023-sebis, title = "Sebis at {S}em{E}val-2023 Task 7: A Joint System for Natural Language Inference and Evidence Retrieval from Clinical Trial Reports", author = "Vladika, Juraj and Matthes, Florian", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.257", doi = "10.18653/v1/2023.semeval-1.257", pages = "1863--1870", abstract = "With the increasing number of clinical trial reports generated every day, it is becoming hard to keep up with novel discoveries that inform evidence-based healthcare recommendations. To help automate this process and assist medical experts, NLP solutions are being developed. This motivated the SemEval-2023 Task 7, where the goal was to develop an NLP system for two tasks: evidence retrieval and natural language inference from clinical trial data. In this paper, we describe our two developed systems. The first one is a pipeline system that models the two tasks separately, while the second one is a joint system that learns the two tasks simultaneously with a shared representation and a multi-task learning approach. The final system combines their outputs in an ensemble system. We formalize the models, present their characteristics and challenges, and provide an analysis of achieved results. Our system ranked 3rd out of 40 participants with a final submission.", }

With the increasing number of clinical trial reports generated every day, it is becoming hard to keep up with novel discoveries that inform evidence-based healthcare recommendations. To help automate this process and assist medical experts, NLP solutions are being developed. This motivated the SemEval-2023 Task 7, where the goal was to develop an NLP system for two tasks: evidence retrieval and natural language inference from clinical trial data. In this paper, we describe our two developed systems. The first one is a pipeline system that models the two tasks separately, while the second one is a joint system that learns the two tasks simultaneously with a shared representation and a multi-task learning approach. The final system combines their outputs in an ensemble system. We formalize the models, present their characteristics and challenges, and provide an analysis of achieved results. Our system ranked 3rd out of 40 participants with a final submission.

Sebis at SemEval-2023 Task 7: A Joint System for Natural Language Inference and Evidence Retrieval from Clinical Trial Reports

semeval-1.257

Poster

2304.13180

https://aclanthology.org/2023.semeval-1.258.bib

https://aclanthology.org/2023.semeval-1.258/

@inproceedings{talavera-cepeda-etal-2023-sren, title = "Sren Kierkegaard at {S}em{E}val-2023 Task 4: Label-aware text classification using Natural Language Inference", author = "Talavera Cepeda, Ignacio and Pauli, Amalie and Assent, Ira", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.258", doi = "10.18653/v1/2023.semeval-1.258", pages = "1871--1877", abstract = "In this paper, we describe our approach to Task 4 in SemEval 2023. Our pipeline tries to solve the problem of multi-label text classification of human values in English-written arguments. We propose a label-aware system where we reframe the multi-label task into a binary task resembling an NLI task. We propose to include the semantic description of the human values by comparing each description to each argument and ask whether there is entailment or not.", }

In this paper, we describe our approach to Task 4 in SemEval 2023. Our pipeline tries to solve the problem of multi-label text classification of human values in English-written arguments. We propose a label-aware system where we reframe the multi-label task into a binary task resembling an NLI task. We propose to include the semantic description of the human values by comparing each description to each argument and ask whether there is entailment or not.

Sren Kierkegaard at SemEval-2023 Task 4: Label-aware text classification using Natural Language Inference

semeval-1.258

Poster

https://aclanthology.org/2023.semeval-1.259.bib

https://aclanthology.org/2023.semeval-1.259/

@inproceedings{sharma-etal-2023-billy, title = "Billy-Batson at {S}em{E}val-2023 Task 5: An Information Condensation based System for Clickbait Spoiling", author = "Sharma, Anubhav and Joshi, Sagar and Abhishek, Tushar and Mamidi, Radhika and Varma, Vasudeva", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.259", doi = "10.18653/v1/2023.semeval-1.259", pages = "1878--1889", abstract = "The Clickbait Challenge targets spoiling the clickbaits using short pieces of information known as spoilers to satisfy the curiosity induced by a clickbait post. The large context of the article associated with the clickbait and differences in the spoiler forms, make the task challenging. Hence, to tackle the large context, we propose an Information Condensation-based approach, which prunes down the unnecessary context. Given an article, our filtering module optimised with a contrastive learning objective first selects the parapraphs that are the most relevant to the corresponding clickbait.The resulting condensed article is then fed to the two downstream tasks of spoiler type classification and spoiler generation. We demonstrate and analyze the gains from this approach on both the tasks. Overall, we win the task of spoiler type classification and achieve competitive results on spoiler generation.", }

The Clickbait Challenge targets spoiling the clickbaits using short pieces of information known as spoilers to satisfy the curiosity induced by a clickbait post. The large context of the article associated with the clickbait and differences in the spoiler forms, make the task challenging. Hence, to tackle the large context, we propose an Information Condensation-based approach, which prunes down the unnecessary context. Given an article, our filtering module optimised with a contrastive learning objective first selects the parapraphs that are the most relevant to the corresponding clickbait.The resulting condensed article is then fed to the two downstream tasks of spoiler type classification and spoiler generation. We demonstrate and analyze the gains from this approach on both the tasks. Overall, we win the task of spoiler type classification and achieve competitive results on spoiler generation.

Billy-Batson at SemEval-2023 Task 5: An Information Condensation based System for Clickbait Spoiling

semeval-1.259

Poster

https://aclanthology.org/2023.semeval-1.260.bib

https://aclanthology.org/2023.semeval-1.260/

@inproceedings{indurthi-varma-2023-francis, title = "{F}rancis Wilde at {S}em{E}val-2023 Task 5: Clickbait Spoiler Type Identification with Transformers", author = "Indurthi, Vijayasaradhi and Varma, Vasudeva", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.260", doi = "10.18653/v1/2023.semeval-1.260", pages = "1890--1893", abstract = "Clickbait is the text or a thumbnail image that entices the user to click the accompanying link. Clickbaits employ strategies while deliberately hiding the critical elements of the article and revealing partial information in the title, which arouses sufficient curiosity and motivates the user to click the link. In this work, we identify the kind of spoiler given a clickbait title. We formulate this as a text classification problem. We finetune pretrained transformer models on the title of the post and build models for theclickbait-spoiler classification. We achieve a balanced accuracy of 0.70 which is close to the baseline.", }

Clickbait is the text or a thumbnail image that entices the user to click the accompanying link. Clickbaits employ strategies while deliberately hiding the critical elements of the article and revealing partial information in the title, which arouses sufficient curiosity and motivates the user to click the link. In this work, we identify the kind of spoiler given a clickbait title. We formulate this as a text classification problem. We finetune pretrained transformer models on the title of the post and build models for theclickbait-spoiler classification. We achieve a balanced accuracy of 0.70 which is close to the baseline.

Francis Wilde at SemEval-2023 Task 5: Clickbait Spoiler Type Identification with Transformers

semeval-1.260

Poster

https://aclanthology.org/2023.semeval-1.261.bib

https://aclanthology.org/2023.semeval-1.261/

@inproceedings{leonardelli-casula-2023-dh, title = "{DH}-{FBK} at {S}em{E}val-2023 Task 10: Multi-Task Learning with Classifier Ensemble Agreement for Sexism Detection", author = "Leonardelli, Elisa and Casula, Camilla", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.261", doi = "10.18653/v1/2023.semeval-1.261", pages = "1894--1905", abstract = "This paper presents the submissions of the DH-FBK team for the three tasks of Task 10 at SemEval 2023. The Explainable Detection of Online Sexism (EDOS) task aims at detecting sexism in English text in an accurate and explainable way, thanks to a fine-grained annotation that follows a three-level schema: sexist or not (Task A), category of sexism (Task B) and vector of sexism (Task C) exhibited. We use a multi-task learning approach in which models share representations from all three tasks, allowing for knowledge to be shared across them. Notably, with our approach a single model can solve all three tasks. In addition, motivated by the subjective nature of the task, we incorporate inter-annotator agreement information in our multi-task architecture. Although disaggregated annotations are not available, we artificially estimate them using a 5-classifier ensemble, and show that ensemble agreement can be a good approximation of crowd agreement. Our approach achieves competitive results, ranking 32nd out of 84, 24th out of 69 and 11th out of 63 for Tasks A, B and C respectively. We finally show that low inter-annotator agreement levels are associated with more challenging examples for models, making agreement information use ful for this kind of task.", }

This paper presents the submissions of the DH-FBK team for the three tasks of Task 10 at SemEval 2023. The Explainable Detection of Online Sexism (EDOS) task aims at detecting sexism in English text in an accurate and explainable way, thanks to a fine-grained annotation that follows a three-level schema: sexist or not (Task A), category of sexism (Task B) and vector of sexism (Task C) exhibited. We use a multi-task learning approach in which models share representations from all three tasks, allowing for knowledge to be shared across them. Notably, with our approach a single model can solve all three tasks. In addition, motivated by the subjective nature of the task, we incorporate inter-annotator agreement information in our multi-task architecture. Although disaggregated annotations are not available, we artificially estimate them using a 5-classifier ensemble, and show that ensemble agreement can be a good approximation of crowd agreement. Our approach achieves competitive results, ranking 32nd out of 84, 24th out of 69 and 11th out of 63 for Tasks A, B and C respectively. We finally show that low inter-annotator agreement levels are associated with more challenging examples for models, making agreement information use ful for this kind of task.

DH-FBK at SemEval-2023 Task 10: Multi-Task Learning with Classifier Ensemble Agreement for Sexism Detection

semeval-1.261

Poster

https://aclanthology.org/2023.semeval-1.262.bib

https://aclanthology.org/2023.semeval-1.262/

@inproceedings{kumar-etal-2023-jack, title = "Jack-flood at {S}em{E}val-2023 Task 5:Hierarchical Encoding and Reciprocal Rank Fusion-Based System for Spoiler Classification and Generation", author = "Kumar, Sujit and Sinha, Aditya and Jana, Soumyadeep and Mishra, Rahul and Singh, Sanasam Ranbir", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.262", doi = "10.18653/v1/2023.semeval-1.262", pages = "1906--1915", abstract = "The rise of social media has exponentially witnessed the use of clickbait posts that grab users{'} attention. Although work has been done to detect clickbait posts, this is the first task focused on generating appropriate spoilers for these potential clickbaits. This paper presents our approach in this direction. We use different encoding techniques that capture the context of the post text and the target paragraph. We propose hierarchical encoding with count and document length feature-based model for spoiler type classification which uses Recurrence over Pretrained Encoding. We also propose combining multiple ranking with reciprocal rank fusion for passage spoiler retrieval and question-answering approach for phrase spoiler retrieval. For multipart spoiler retrieval, we combine the above two spoiler retrieval methods. Experimental results over the benchmark suggest that our proposed spoiler retrieval methods are able to retrieve spoilers that are semantically very close to the ground truth spoilers.", }

The rise of social media has exponentially witnessed the use of clickbait posts that grab users{'} attention. Although work has been done to detect clickbait posts, this is the first task focused on generating appropriate spoilers for these potential clickbaits. This paper presents our approach in this direction. We use different encoding techniques that capture the context of the post text and the target paragraph. We propose hierarchical encoding with count and document length feature-based model for spoiler type classification which uses Recurrence over Pretrained Encoding. We also propose combining multiple ranking with reciprocal rank fusion for passage spoiler retrieval and question-answering approach for phrase spoiler retrieval. For multipart spoiler retrieval, we combine the above two spoiler retrieval methods. Experimental results over the benchmark suggest that our proposed spoiler retrieval methods are able to retrieve spoilers that are semantically very close to the ground truth spoilers.

Jack-flood at SemEval-2023 Task 5:Hierarchical Encoding and Reciprocal Rank Fusion-Based System for Spoiler Classification and Generation

semeval-1.262

Poster

https://aclanthology.org/2023.semeval-1.263.bib

https://aclanthology.org/2023.semeval-1.263/

@inproceedings{tasneem-etal-2023-kingsmantrio, title = "{K}ingsman{T}rio at {S}em{E}val-2023 Task 10: Analyzing the Effectiveness of Transfer Learning Models for Explainable Online Sexism Detection", author = "Tasneem, Fareen and Hossain, Tashin and Naim, Jannatun", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.263", doi = "10.18653/v1/2023.semeval-1.263", pages = "1916--1920", abstract = "Online social platforms are now propagating sexist content endangering the involvement and inclusion of women on these platforms. Sexism refers to hostility, bigotry, or discrimination based on gender, typically against women. The proliferation of such notions deters women from engaging in social media spontaneously. Hence, detecting sexist content is critical to ensure a safe online platform where women can participate without the fear of being a target of sexism. This paper describes our participation in subtask A of SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). This subtask requires classifying textual content as sexist or not sexist. We incorporate a RoBERTa-based architecture and further finetune the hyperparameters to entail better performance. The procured results depict the competitive performance of our approach among the other participants.", }

Online social platforms are now propagating sexist content endangering the involvement and inclusion of women on these platforms. Sexism refers to hostility, bigotry, or discrimination based on gender, typically against women. The proliferation of such notions deters women from engaging in social media spontaneously. Hence, detecting sexist content is critical to ensure a safe online platform where women can participate without the fear of being a target of sexism. This paper describes our participation in subtask A of SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). This subtask requires classifying textual content as sexist or not sexist. We incorporate a RoBERTa-based architecture and further finetune the hyperparameters to entail better performance. The procured results depict the competitive performance of our approach among the other participants.

KingsmanTrio at SemEval-2023 Task 10: Analyzing the Effectiveness of Transfer Learning Models for Explainable Online Sexism Detection

semeval-1.263

Poster

https://aclanthology.org/2023.semeval-1.264.bib

https://aclanthology.org/2023.semeval-1.264/

@inproceedings{molavi-zeinali-2023-slt, title = "{SLT} at {S}em{E}val-2023 Task 1: Enhancing Visual Word Sense Disambiguation through Image Text Retrieval using {BLIP}", author = "Molavi, Mohammadreza and Zeinali, Hossein", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.264", doi = "10.18653/v1/2023.semeval-1.264", pages = "1921--1925", abstract = "Based on recent progress in image-text retrieval techniques, this paper presents a fine-tuned model for the Visual Word Sense Disambiguation (VWSD) task. The proposed system fine-tunes a pre-trained model using ITC and ITM losses and employs a candidate selection approach for faster inference. The system was trained on the VWSD task dataset and evaluated on a separate test set using Mean Reciprocal Rank (MRR) metric. Additionally, the system was tested on the provided test set which contained Persian and Italian languages, and the results were evaluated on each language separately. Our proposed system demonstrates the potential of fine-tuning pre-trained models for complex language tasks and provides insights for further research in the field of image text retrieval.", }

Based on recent progress in image-text retrieval techniques, this paper presents a fine-tuned model for the Visual Word Sense Disambiguation (VWSD) task. The proposed system fine-tunes a pre-trained model using ITC and ITM losses and employs a candidate selection approach for faster inference. The system was trained on the VWSD task dataset and evaluated on a separate test set using Mean Reciprocal Rank (MRR) metric. Additionally, the system was tested on the provided test set which contained Persian and Italian languages, and the results were evaluated on each language separately. Our proposed system demonstrates the potential of fine-tuning pre-trained models for complex language tasks and provides insights for further research in the field of image text retrieval.

SLT at SemEval-2023 Task 1: Enhancing Visual Word Sense Disambiguation through Image Text Retrieval using BLIP

semeval-1.264

Poster

https://aclanthology.org/2023.semeval-1.265.bib

https://aclanthology.org/2023.semeval-1.265/

@inproceedings{n-etal-2023-cair, title = "{CAIR}-{NLP} at {S}em{E}val-2023 Task 2: A Multi-Objective Joint Learning System for Named Entity Recognition", author = "N, Sangeeth and Paul, Biswajit and Chaudhary, Chandramani", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.265", doi = "10.18653/v1/2023.semeval-1.265", pages = "1926--1935", abstract = "This paper describes the NER system designed by the CAIR-NLP team for submission to Multilingual Complex Named Entity Recognition (MultiCoNER II) shared task, which presents a novel challenge of recognizing complex, ambiguous, and fine-grained entities in low-context, multi-lingual, multi-domain dataset and evaluation on the noisy subset. We propose a Multi-Objective Joint Learning System (MOJLS) for NER, which aims to enhance the representation of entities and improve label predictions through the joint implementation of a set of learning objectives. Our official submission MOJLS implements four objectives. These include the representation of the named entities should be close to its entity type definition, low-context inputs should have representation close to their augmented context, and also minimization of two label prediction errors, one based on CRF and another biaffine-based predictions, where both are producing similar output label distributions. The official results ranked our system 2nd in five tracks (Multilingual, Spanish, Swedish, Ukrainian, and Farsi) and 3 rd in three (French, Italian, and Portuguese) out of 13 tracks. Also evaluation of the noisy subset, our model achieved relatively better ranks. Official results indicate the effectiveness of the proposed MOJLS in dealing with the contemporary challenges of NER.", }

This paper describes the NER system designed by the CAIR-NLP team for submission to Multilingual Complex Named Entity Recognition (MultiCoNER II) shared task, which presents a novel challenge of recognizing complex, ambiguous, and fine-grained entities in low-context, multi-lingual, multi-domain dataset and evaluation on the noisy subset. We propose a Multi-Objective Joint Learning System (MOJLS) for NER, which aims to enhance the representation of entities and improve label predictions through the joint implementation of a set of learning objectives. Our official submission MOJLS implements four objectives. These include the representation of the named entities should be close to its entity type definition, low-context inputs should have representation close to their augmented context, and also minimization of two label prediction errors, one based on CRF and another biaffine-based predictions, where both are producing similar output label distributions. The official results ranked our system 2nd in five tracks (Multilingual, Spanish, Swedish, Ukrainian, and Farsi) and 3 rd in three (French, Italian, and Portuguese) out of 13 tracks. Also evaluation of the noisy subset, our model achieved relatively better ranks. Official results indicate the effectiveness of the proposed MOJLS in dealing with the contemporary challenges of NER.

CAIR-NLP at SemEval-2023 Task 2: A Multi-Objective Joint Learning System for Named Entity Recognition

semeval-1.265

Poster

https://aclanthology.org/2023.semeval-1.266.bib

https://aclanthology.org/2023.semeval-1.266/

@inproceedings{pahwa-pahwa-2023-bphigh, title = "{B}p{H}igh at {S}em{E}val-2023 Task 7: Can Fine-tuned Cross-encoders Outperform {GPT}-3.5 in {NLI} Tasks on Clinical Trial Data?", author = "Pahwa, Bhavish and Pahwa, Bhavika", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.266", doi = "10.18653/v1/2023.semeval-1.266", pages = "1936--1944", abstract = "Many nations and organizations have begun collecting and storing clinical trial records for storage and analytical purposes so that medical and clinical practitioners can refer to them on a centralized database over the internet and stay updated with the current clinical information. The amount of clinical trial records have gone through the roof, making it difficult for many medical and clinical practitioners to stay updated with the latest information. To help and support medical and clinical practitioners, there is a need to build intelligent systems that can update them with the latest information in a byte-sized condensed format and, at the same time, leverage their understanding capabilities to help them make decisions. This paper describes our contribution to SemEval 2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT). Our results show that there is still a need to build domain-specific models as smaller transformer-based models can be finetuned on that data and outperform foundational large language models like GPT-3.5. We also demonstrate how the performance of GPT-3.5 can be increased using few-shot prompting by leveraging the semantic similarity of the text samples and the few-shot train snippets. We will also release our code and our models on open source hosting platforms, GitHub and HuggingFace.", }

Many nations and organizations have begun collecting and storing clinical trial records for storage and analytical purposes so that medical and clinical practitioners can refer to them on a centralized database over the internet and stay updated with the current clinical information. The amount of clinical trial records have gone through the roof, making it difficult for many medical and clinical practitioners to stay updated with the latest information. To help and support medical and clinical practitioners, there is a need to build intelligent systems that can update them with the latest information in a byte-sized condensed format and, at the same time, leverage their understanding capabilities to help them make decisions. This paper describes our contribution to SemEval 2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT). Our results show that there is still a need to build domain-specific models as smaller transformer-based models can be finetuned on that data and outperform foundational large language models like GPT-3.5. We also demonstrate how the performance of GPT-3.5 can be increased using few-shot prompting by leveraging the semantic similarity of the text samples and the few-shot train snippets. We will also release our code and our models on open source hosting platforms, GitHub and HuggingFace.

BpHigh at SemEval-2023 Task 7: Can Fine-tuned Cross-encoders Outperform GPT-3.5 in NLI Tasks on Clinical Trial Data?

semeval-1.266

Poster

https://aclanthology.org/2023.semeval-1.267.bib

https://aclanthology.org/2023.semeval-1.267/

@inproceedings{suri-etal-2023-wader, title = "{WADER} at {S}em{E}val-2023 Task 9: A Weak-labelling framework for Data augmentation in t{E}xt Regression Tasks", author = "Suri, Manan and Garg, Aaryak and Chaudhary, Divya and Gorton, Ian and Kumar, Bijendra", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.267", doi = "10.18653/v1/2023.semeval-1.267", pages = "1945--1952", abstract = "Intimacy is an essential element of human relationships and language is a crucial means of conveying it. Textual intimacy analysis can reveal social norms in different contexts and serve as a benchmark for testing computational models{'} ability to understand social information. In this paper, we propose a novel weak-labeling strategy for data augmentation in text regression tasks called WADER. WADER uses data augmentation to address the problems of data imbalance and data scarcity and provides a method for data augmentation in cross-lingual, zero-shot tasks. We benchmark the performance of State-of-the-Art pre-trained multilingual language models using WADER and analyze the use of sampling techniques to mitigate bias in data and optimally select augmentation candidates. Our results show that WADER outperforms the baseline model and provides a direction for mitigating data imbalance and scarcity in text regression tasks.", }

Intimacy is an essential element of human relationships and language is a crucial means of conveying it. Textual intimacy analysis can reveal social norms in different contexts and serve as a benchmark for testing computational models{'} ability to understand social information. In this paper, we propose a novel weak-labeling strategy for data augmentation in text regression tasks called WADER. WADER uses data augmentation to address the problems of data imbalance and data scarcity and provides a method for data augmentation in cross-lingual, zero-shot tasks. We benchmark the performance of State-of-the-Art pre-trained multilingual language models using WADER and analyze the use of sampling techniques to mitigate bias in data and optimally select augmentation candidates. Our results show that WADER outperforms the baseline model and provides a direction for mitigating data imbalance and scarcity in text regression tasks.

WADER at SemEval-2023 Task 9: A Weak-labelling framework for Data augmentation in tExt Regression Tasks

semeval-1.267

Poster

2303.02758

https://aclanthology.org/2023.semeval-1.268.bib

https://aclanthology.org/2023.semeval-1.268/

@inproceedings{song-etal-2023-arthur, title = "Arthur Caplan at {S}em{E}val-2023 Task 4: Enhancing Human Value Detection through Fine-tuned Pre-trained Models", author = "Song, Xianxian and Zhao, Jinhui and Cao, Ruiqi and Sui, Linchi and Li, Binyang and Guan, Tingyue", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.268", doi = "10.18653/v1/2023.semeval-1.268", pages = "1953--1959", abstract = "The computational identification of human values is a novel and challenging research that holds the potential to offer valuable insights into the nature of human behavior and cognition. This paper presents the methodology adopted by the Arthur-Caplan research team for the SemEval-2023 Task 4, which entailed the detection of human values behind arguments. The proposed system integrates BERT, ERNIE2.0, RoBERTA and XLNet models with fine tuning. Experimental results show that the macro F1 score of our system achieved 0.512, which overperformed baseline methods by 9.2{\%} on the test set.", }

The computational identification of human values is a novel and challenging research that holds the potential to offer valuable insights into the nature of human behavior and cognition. This paper presents the methodology adopted by the Arthur-Caplan research team for the SemEval-2023 Task 4, which entailed the detection of human values behind arguments. The proposed system integrates BERT, ERNIE2.0, RoBERTA and XLNet models with fine tuning. Experimental results show that the macro F1 score of our system achieved 0.512, which overperformed baseline methods by 9.2{\%} on the test set.

Arthur Caplan at SemEval-2023 Task 4: Enhancing Human Value Detection through Fine-tuned Pre-trained Models

semeval-1.268

Poster

https://aclanthology.org/2023.semeval-1.269.bib

https://aclanthology.org/2023.semeval-1.269/

@inproceedings{taghavi-etal-2023-ebhaam, title = "Ebhaam at {S}em{E}val-2023 Task 1: A {CLIP}-Based Approach for Comparing Cross-modality and Unimodality in Visual Word Sense Disambiguation", author = "Taghavi, Zeinab and Naeini, Parsa Haghighi and Sadraei Javaheri, Mohammad Ali and Gooran, Soroush and Asgari, Ehsaneddin and Rabiee, Hamid Reza and Sameti, Hossein", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.269", doi = "10.18653/v1/2023.semeval-1.269", pages = "1960--1964", abstract = "This paper presents an approach to tackle the task of Visual Word Sense Disambiguation (Visual-WSD), which involves determining the most appropriate image to represent a given polysemous word in one of its particular senses. The proposed approach leverages the CLIP model, prompt engineering, and text-to-image models such as GLIDE and DALL-E 2 for both image retrieval and generation. To evaluate our approach, we participated in the SemEval 2023 shared task on {``}Visual Word Sense Disambiguation (Visual-WSD){''} using a zero-shot learning setting, where we compared the accuracy of different combinations of tools, including {``}Simple prompt-based{''} methods and {``}Generated prompt-based{''} methods for prompt engineering using completion models, and text-to-image models for changing input modality from text to image. Moreover, we explored the benefits of cross-modality evaluation between text and candidate images using CLIP. Our experimental results demonstrate that the proposed approach reaches better results than cross-modality approaches, highlighting the potential of prompt engineering and text-to-image models to improve accuracy in Visual-WSD tasks. We assessed our approach in a zero-shot learning scenario and attained an accuracy of 68.75{\textbackslash}{\%} in our best attempt.", }

This paper presents an approach to tackle the task of Visual Word Sense Disambiguation (Visual-WSD), which involves determining the most appropriate image to represent a given polysemous word in one of its particular senses. The proposed approach leverages the CLIP model, prompt engineering, and text-to-image models such as GLIDE and DALL-E 2 for both image retrieval and generation. To evaluate our approach, we participated in the SemEval 2023 shared task on {``}Visual Word Sense Disambiguation (Visual-WSD){''} using a zero-shot learning setting, where we compared the accuracy of different combinations of tools, including {``}Simple prompt-based{''} methods and {``}Generated prompt-based{''} methods for prompt engineering using completion models, and text-to-image models for changing input modality from text to image. Moreover, we explored the benefits of cross-modality evaluation between text and candidate images using CLIP. Our experimental results demonstrate that the proposed approach reaches better results than cross-modality approaches, highlighting the potential of prompt engineering and text-to-image models to improve accuracy in Visual-WSD tasks. We assessed our approach in a zero-shot learning scenario and attained an accuracy of 68.75{\textbackslash}{\%} in our best attempt.

Ebhaam at SemEval-2023 Task 1: A CLIP-Based Approach for Comparing Cross-modality and Unimodality in Visual Word Sense Disambiguation

semeval-1.269

Poster

https://aclanthology.org/2023.semeval-1.270.bib

https://aclanthology.org/2023.semeval-1.270/

@inproceedings{berend-2023-szegedai, title = "{S}zeged{AI} at {S}em{E}val-2023 Task 1: Applying Quasi-Symbolic Representations in Visual Word Sense Disambiguation", author = "Berend, G{\'a}bor", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.270", doi = "10.18653/v1/2023.semeval-1.270", pages = "1965--1971", abstract = "In this paper, we introduce our submission in the task of visual word sense disambiguation (vWSD). Our proposed solution operates by deriving quasi-symbolic semantic categories from the hidden representations of multi-modal text-image encoders. Our results are mixed, as we manage to achieve a substantial boost in performance when evaluating on a validation set, however, we experienced detrimental effects during evaluation on the actual test set. Our positive results on the validation set confirms the validity of the quasi-symbolic features, whereas our results on the test set revealed that the proposed technique was not able to cope with the sufficiently different distribution of the test data.", }

In this paper, we introduce our submission in the task of visual word sense disambiguation (vWSD). Our proposed solution operates by deriving quasi-symbolic semantic categories from the hidden representations of multi-modal text-image encoders. Our results are mixed, as we manage to achieve a substantial boost in performance when evaluating on a validation set, however, we experienced detrimental effects during evaluation on the actual test set. Our positive results on the validation set confirms the validity of the quasi-symbolic features, whereas our results on the test set revealed that the proposed technique was not able to cope with the sufficiently different distribution of the test data.

SzegedAI at SemEval-2023 Task 1: Applying Quasi-Symbolic Representations in Visual Word Sense Disambiguation

semeval-1.270

Poster

https://aclanthology.org/2023.semeval-1.271.bib

https://aclanthology.org/2023.semeval-1.271/

@inproceedings{roy-shrivastava-2023-attention, title = "Attention at {S}em{E}val-2023 Task 10: Explainable Detection of Online Sexism ({EDOS})", author = "Roy, Debashish and Shrivastava, Manish", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.271", doi = "10.18653/v1/2023.semeval-1.271", pages = "1972--1977", abstract = "In this paper, we have worked on explainability and understanding of the decisions made by models in the form of classification tasks. The task is divided into 3 subtasks. The first task consists of determining Binary Sexism Detection. The second task describes the Category of Sexism. The third task describes a more Fine-grained Category of Sexism. Our work explores solving these tasks as a classification problem by fine-tuning transformer-based architecture. We have performed several experiments with our architecture, including combining multiple transformers, using domain adaptive pretraining on the unlabelled dataset provided by Reddit and Gab, Joint learning, and taking different layers of transformers as input to a classification head. Our system (with the team name Attention{'}) was able to achieve a macro F1 score of 0.839 for task A, 0.5835 macro F1 score for task B and 0.3356 macro F1 score for task C at the Codalab SemEval Competition. Later we improved the accuracy of Task B to 0.6228 and Task C to 0.3693 in the test set.", }

In this paper, we have worked on explainability and understanding of the decisions made by models in the form of classification tasks. The task is divided into 3 subtasks. The first task consists of determining Binary Sexism Detection. The second task describes the Category of Sexism. The third task describes a more Fine-grained Category of Sexism. Our work explores solving these tasks as a classification problem by fine-tuning transformer-based architecture. We have performed several experiments with our architecture, including combining multiple transformers, using domain adaptive pretraining on the unlabelled dataset provided by Reddit and Gab, Joint learning, and taking different layers of transformers as input to a classification head. Our system (with the team name Attention{'}) was able to achieve a macro F1 score of 0.839 for task A, 0.5835 macro F1 score for task B and 0.3356 macro F1 score for task C at the Codalab SemEval Competition. Later we improved the accuracy of Task B to 0.6228 and Task C to 0.3693 in the test set.

Attention at SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS)

semeval-1.271

Poster

2304.04610

https://aclanthology.org/2023.semeval-1.272.bib

https://aclanthology.org/2023.semeval-1.272/

@inproceedings{wan-badillo-urquiola-2023-dragonfly, title = "Dragonfly{\_}captain at {S}em{E}val-2023 Task 11: Unpacking Disagreement with Investigation of Annotator Demographics and Task Difficulty", author = "Wan, Ruyuan and Badillo-Urquiola, Karla", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.272", doi = "10.18653/v1/2023.semeval-1.272", pages = "1978--1982", abstract = "This study investigates learning with disagreement in NLP tasks and evaluates its performance on four datasets. The results suggest that the model performs best on the experimental dataset and faces challenges in minority languages. Furthermore, the analysis indicates that annotator demographics play a significant role in the interpretation of such tasks. This study suggests the need for greater consideration of demographic differences in annotators and more comprehensive evaluation metrics for NLP models.", }

This study investigates learning with disagreement in NLP tasks and evaluates its performance on four datasets. The results suggest that the model performs best on the experimental dataset and faces challenges in minority languages. Furthermore, the analysis indicates that annotator demographics play a significant role in the interpretation of such tasks. This study suggests the need for greater consideration of demographic differences in annotators and more comprehensive evaluation metrics for NLP models.

Dragonfly_captain at SemEval-2023 Task 11: Unpacking Disagreement with Investigation of Annotator Demographics and Task Difficulty

semeval-1.272

Poster

https://aclanthology.org/2023.semeval-1.273.bib

https://aclanthology.org/2023.semeval-1.273/

@inproceedings{aliyu-etal-2023-hausanlp, title = "{H}ausa{NLP} at {S}em{E}val-2023 Task 10: Transfer Learning, Synthetic Data and Side-information for Multi-level Sexism Classification", author = "Aliyu, Saminu Mohammad and Abdulmumin, Idris and Muhammad, Shamsuddeen Hassan and Ahmad, Ibrahim Said and Salahudeen, Saheed Abdullahi and Yusuf, Aliyu and Lawan, Falalu Ibrahim", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.273", doi = "10.18653/v1/2023.semeval-1.273", pages = "1983--1987", abstract = "We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) task, a shared task on offensive language (sexism) detection on English Gab and Reddit dataset. We investigated the effects of transferring two language models: XLM-T (sentiment classification) and HateBERT (same domain - Reddit) for multilevel classification into Sexist or not Sexist, and other subsequent sub-classifications of the sexist data. We also use synthetic classification of unlabelled dataset and intermediary class information to maximize the performance of our models. We submitted a system in Task A, and it ranked 49th with F1-score of 0.82. This result showed to be competitive as it only under-performed the best system by 0.052{\%}F1-score.", }

We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) task, a shared task on offensive language (sexism) detection on English Gab and Reddit dataset. We investigated the effects of transferring two language models: XLM-T (sentiment classification) and HateBERT (same domain - Reddit) for multilevel classification into Sexist or not Sexist, and other subsequent sub-classifications of the sexist data. We also use synthetic classification of unlabelled dataset and intermediary class information to maximize the performance of our models. We submitted a system in Task A, and it ranked 49th with F1-score of 0.82. This result showed to be competitive as it only under-performed the best system by 0.052{\%}F1-score.

HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-information for Multi-level Sexism Classification

semeval-1.273

Poster

2305.00076

https://aclanthology.org/2023.semeval-1.274.bib

https://aclanthology.org/2023.semeval-1.274/

@inproceedings{aziz-etal-2023-csecu, title = "{CSECU}-{DSG} at {S}em{E}val-2023 Task 4: Fine-tuning {D}e{BERT}a Transformer Model with Cross-fold Training and Multi-sample Dropout for Human Values Identification", author = "Aziz, Abdul and Hossain, Md. Akram and Chy, Abu Nowshed", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.274", doi = "10.18653/v1/2023.semeval-1.274", pages = "1988--1994", abstract = "Human values identification from a set of argument is becoming a prominent area of research in argument mining. Among some options, values convey what may be the most desirable and widely accepted answer. The diversity of human beliefs, random texture and implicit meaning within the arguments makes it more difficult to identify human values from the arguments. To address these challenges, SemEval-2023 Task 4 introduced a shared task ValueEval focusing on identifying human values categories based on given arguments. This paper presents our participation in this task where we propose a finetuned DeBERTa transformers-based classification approach to identify the desire human value category. We utilize different training strategy with the finetuned DeBERTa model to enhance contextual representation on this downstream task. Our proposed method achieved competitive performance among the participants{'} methods.", }

Human values identification from a set of argument is becoming a prominent area of research in argument mining. Among some options, values convey what may be the most desirable and widely accepted answer. The diversity of human beliefs, random texture and implicit meaning within the arguments makes it more difficult to identify human values from the arguments. To address these challenges, SemEval-2023 Task 4 introduced a shared task ValueEval focusing on identifying human values categories based on given arguments. This paper presents our participation in this task where we propose a finetuned DeBERTa transformers-based classification approach to identify the desire human value category. We utilize different training strategy with the finetuned DeBERTa model to enhance contextual representation on this downstream task. Our proposed method achieved competitive performance among the participants{'} methods.

CSECU-DSG at SemEval-2023 Task 4: Fine-tuning DeBERTa Transformer Model with Cross-fold Training and Multi-sample Dropout for Human Values Identification

semeval-1.274

Poster

https://aclanthology.org/2023.semeval-1.275.bib

https://aclanthology.org/2023.semeval-1.275/

@inproceedings{wu-etal-2023-sheffieldveraai, title = "{S}heffield{V}era{AI} at {S}em{E}val-2023 Task 3: Mono and Multilingual Approaches for News Genre, Topic and Persuasion Technique Classification", author = "Wu, Ben and Razuvayevskaya, Olesya and Heppell, Freddy and Leite, Jo{\~a}o A. and Scarton, Carolina and Bontcheva, Kalina and Song, Xingyi", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.275", doi = "10.18653/v1/2023.semeval-1.275", pages = "1995--2008", abstract = "This paper describes our approach for SemEval- 2023 Task 3: Detecting the category, the fram- ing, and the persuasion techniques in online news in a multilingual setup. For Subtask 1 (News Genre), we propose an ensemble of fully trained and adapter mBERT models which was ranked joint-first for German, and had the high- est mean rank of multi-language teams. For Subtask 2 (Framing), we achieved first place in 3 languages, and the best average rank across all the languages, by using two separate ensem- bles: a monolingual RoBERTa-MUPPETLARGE and an ensemble of XLM-RoBERTaLARGE with adapters and task adaptive pretraining. For Sub- task 3 (Persuasion Techniques), we trained a monolingual RoBERTa-Base model for English and a multilingual mBERT model for the re- maining languages, which achieved top 10 for all languages, including 2nd for English. For each subtask, we compared monolingual and multilingual approaches, and considered class imbalance techniques.", }

This paper describes our approach for SemEval- 2023 Task 3: Detecting the category, the fram- ing, and the persuasion techniques in online news in a multilingual setup. For Subtask 1 (News Genre), we propose an ensemble of fully trained and adapter mBERT models which was ranked joint-first for German, and had the high- est mean rank of multi-language teams. For Subtask 2 (Framing), we achieved first place in 3 languages, and the best average rank across all the languages, by using two separate ensem- bles: a monolingual RoBERTa-MUPPETLARGE and an ensemble of XLM-RoBERTaLARGE with adapters and task adaptive pretraining. For Sub- task 3 (Persuasion Techniques), we trained a monolingual RoBERTa-Base model for English and a multilingual mBERT model for the re- maining languages, which achieved top 10 for all languages, including 2nd for English. For each subtask, we compared monolingual and multilingual approaches, and considered class imbalance techniques.

SheffieldVeraAI at SemEval-2023 Task 3: Mono and Multilingual Approaches for News Genre, Topic and Persuasion Technique Classification

semeval-1.275

Poster

2303.09421

https://aclanthology.org/2023.semeval-1.276.bib

https://aclanthology.org/2023.semeval-1.276/

@inproceedings{b-etal-2023-ckingcoder, title = "{CK}ing{C}oder at {S}em{E}val-2023 Task 9: Multilingual Tweet Intimacy Analysis", author = "B, Harish and D, Naveen and Balasubramanian, Prem and S, Aarthi", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.276", doi = "10.18653/v1/2023.semeval-1.276", pages = "2009--2013", abstract = "The SemEval 2023 Task 9 Multilingual Tweet Intimacy Analysis, is a shared task for analysing the intimacy in the tweets posted on Twitter. The dataset was provided by Pei and Jurgens, who are part of the task organisers, for this task consists of tweets in various languages, such as Chinese, English, French, Italian, Portuguese, and Spanish. The testing dataset also had unseen languages such as Hindi, Arabic, Dutch and Korean. The tweets may or may not be related to intimacy. The task of our team was to score the intimacy in tweets and place it in the range of 05 based on the level of intimacy in the tweet using the dataset provided which consisted of tweets along with its scores. The intimacy score is used to indicate whether a tweet is intimate or not. Our team participated in the task and proposed the ROBERTa model to analyse the intimacy of the tweets.", }

The SemEval 2023 Task 9 Multilingual Tweet Intimacy Analysis, is a shared task for analysing the intimacy in the tweets posted on Twitter. The dataset was provided by Pei and Jurgens, who are part of the task organisers, for this task consists of tweets in various languages, such as Chinese, English, French, Italian, Portuguese, and Spanish. The testing dataset also had unseen languages such as Hindi, Arabic, Dutch and Korean. The tweets may or may not be related to intimacy. The task of our team was to score the intimacy in tweets and place it in the range of 05 based on the level of intimacy in the tweet using the dataset provided which consisted of tweets along with its scores. The intimacy score is used to indicate whether a tweet is intimate or not. Our team participated in the task and proposed the ROBERTa model to analyse the intimacy of the tweets.

CKingCoder at SemEval-2023 Task 9: Multilingual Tweet Intimacy Analysis

semeval-1.276

Poster

https://aclanthology.org/2023.semeval-1.277.bib

https://aclanthology.org/2023.semeval-1.277/

@inproceedings{tan-etal-2023-damo, title = "{DAMO}-{NLP} at {S}em{E}val-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition", author = "Tan, Zeqi and Huang, Shen and Jia, Zixia and Cai, Jiong and Li, Yinghui and Lu, Weiming and Zhuang, Yueting and Tu, Kewei and Xie, Pengjun and Huang, Fei and Jiang, Yong", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.277", doi = "10.18653/v1/2023.semeval-1.277", pages = "2014--2028", abstract = "The MultiCoNER II shared task aims to tackle multilingual named entity recognition (NER) in fine-grained and noisy scenarios, and it inherits the semantic ambiguity and low-context setting of the MultiCoNER I task. To cope with these problems, the previous top systems in the MultiCoNER I either incorporate the knowledge bases or gazetteers. However, they still suffer from insufficient knowledge, limited context length, single retrieval strategy. In this paper, our team DAMO-NLP proposes a unified retrieval-augmented system (U-RaNER) for fine-grained multilingual NER. We perform error analysis on the previous top systems and reveal that their performance bottleneck lies in insufficient knowledge. Also, we discover that the limited context length causes the retrieval knowledge to be invisible to the model. To enhance the retrieval context, we incorporate the entity-centric Wikidata knowledge base, while utilizing the infusion approach to broaden the contextual scope of the model. Also, we explore various search strategies and refine the quality of retrieval knowledge. Our system wins 9 out of 13 tracks in the MultiCoNER II shared task. Additionally, we compared our system with ChatGPT, one of the large language models which have unlocked strong capabilities on many tasks. The results show that there is still much room for improvement for ChatGPT on the extraction task.", }

The MultiCoNER II shared task aims to tackle multilingual named entity recognition (NER) in fine-grained and noisy scenarios, and it inherits the semantic ambiguity and low-context setting of the MultiCoNER I task. To cope with these problems, the previous top systems in the MultiCoNER I either incorporate the knowledge bases or gazetteers. However, they still suffer from insufficient knowledge, limited context length, single retrieval strategy. In this paper, our team DAMO-NLP proposes a unified retrieval-augmented system (U-RaNER) for fine-grained multilingual NER. We perform error analysis on the previous top systems and reveal that their performance bottleneck lies in insufficient knowledge. Also, we discover that the limited context length causes the retrieval knowledge to be invisible to the model. To enhance the retrieval context, we incorporate the entity-centric Wikidata knowledge base, while utilizing the infusion approach to broaden the contextual scope of the model. Also, we explore various search strategies and refine the quality of retrieval knowledge. Our system wins 9 out of 13 tracks in the MultiCoNER II shared task. Additionally, we compared our system with ChatGPT, one of the large language models which have unlocked strong capabilities on many tasks. The results show that there is still much room for improvement for ChatGPT on the extraction task.

DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition

semeval-1.277

Poster

2305.03688

https://aclanthology.org/2023.semeval-1.278.bib

https://aclanthology.org/2023.semeval-1.278/

@inproceedings{rostamkhani-etal-2023-rozam, title = "{ROZAM} at {S}em{E}val 2023 Task 9: Multilingual Tweet Intimacy Analysis", author = "Rostamkhani, Mohammadmostafa and Zamaninejad, Ghazal and Eetemadi, Sauleh", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.278", doi = "10.18653/v1/2023.semeval-1.278", pages = "2029--2032", abstract = "We build a model using large multilingual pretrained language model XLM-T for regression task and fine-tune it on the MINT (Multilingual INTmacy) analysis dataset which covers 6 languages for training and 4 languages for testing zero-shot performance of the model. The dataset was annotated and the annotations are intimacy scores. We experiment with several deep learning architectures to predict intimacy score. To achieve optimal performance we modify several model settings including loss function, number and type of layers. In total, we ran 16 end-to-end experiments. Our best system achieved a Pearson Correlation score of 0.52.", }

We build a model using large multilingual pretrained language model XLM-T for regression task and fine-tune it on the MINT (Multilingual INTmacy) analysis dataset which covers 6 languages for training and 4 languages for testing zero-shot performance of the model. The dataset was annotated and the annotations are intimacy scores. We experiment with several deep learning architectures to predict intimacy score. To achieve optimal performance we modify several model settings including loss function, number and type of layers. In total, we ran 16 end-to-end experiments. Our best system achieved a Pearson Correlation score of 0.52.

ROZAM at SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis

semeval-1.278

Poster

https://aclanthology.org/2023.semeval-1.279.bib

https://aclanthology.org/2023.semeval-1.279/

@inproceedings{moosavi-monazzah-eetemadi-2023-prodicus, title = "Prodicus at {S}em{E}val-2023 Task 4: Enhancing Human Value Detection with Data Augmentation and Fine-Tuned Language Models", author = "Moosavi Monazzah, Erfan and Eetemadi, Sauleh", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.279", doi = "10.18653/v1/2023.semeval-1.279", pages = "2033--2038", abstract = "This paper introduces a data augmentation technique for the task of detecting human values. Our approach involves generating additional examples using metadata that describes the labels in the datasets. We evaluated the effectiveness of our method by fine-tuning BERT and RoBERTa models on our augmented dataset and comparing their F1 -scores to those of the non-augmented dataset. We obtained competitive results on both the Main test set and the Nahj al-Balagha test set, ranking 14th and 7th respectively among the participants. We also demonstrate that by incorporating our augmentation technique, the classification performance of BERT and RoBERTa is improved, resulting in an increase of up to 10.1{\%} in their F1-score.", }

This paper introduces a data augmentation technique for the task of detecting human values. Our approach involves generating additional examples using metadata that describes the labels in the datasets. We evaluated the effectiveness of our method by fine-tuning BERT and RoBERTa models on our augmented dataset and comparing their F1 -scores to those of the non-augmented dataset. We obtained competitive results on both the Main test set and the Nahj al-Balagha test set, ranking 14th and 7th respectively among the participants. We also demonstrate that by incorporating our augmentation technique, the classification performance of BERT and RoBERTa is improved, resulting in an increase of up to 10.1{\%} in their F1-score.

Prodicus at SemEval-2023 Task 4: Enhancing Human Value Detection with Data Augmentation and Fine-Tuned Language Models

semeval-1.279

Poster

https://aclanthology.org/2023.semeval-1.280.bib

https://aclanthology.org/2023.semeval-1.280/

@inproceedings{hasanaliyev-etal-2023-francis, title = "{F}rancis Bacon at {S}em{E}val-2023 Task 4: Ensembling {BERT} and {G}lo{V}e for Value Identification in Arguments", author = "Hasanaliyev, Kenan and Li, Kevin and Chawla, Saanvi and Nath, Michael and Sanda, Rohan and Wu, Justin and Huang, William and Yang, Daniel and Mion, Shane and Bhat, Kiran", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.280", doi = "10.18653/v1/2023.semeval-1.280", pages = "2039--2042", abstract = "In this paper, we discuss our efforts on SemEval-2023 Task4, a task to classify the human value categoriesthat an argument draws on. Arguments consist of a premise, conclusion,and the premise{'}s stance on the conclusion. Our team experimented with GloVe embeddings and fine-tuning BERT. We found that an ensembling of BERT and GloVe with RidgeRegression worked the best.", }

In this paper, we discuss our efforts on SemEval-2023 Task4, a task to classify the human value categoriesthat an argument draws on. Arguments consist of a premise, conclusion,and the premise{'}s stance on the conclusion. Our team experimented with GloVe embeddings and fine-tuning BERT. We found that an ensembling of BERT and GloVe with RidgeRegression worked the best.

Francis Bacon at SemEval-2023 Task 4: Ensembling BERT and GloVe for Value Identification in Arguments

semeval-1.280

Poster

https://aclanthology.org/2023.semeval-1.281.bib

https://aclanthology.org/2023.semeval-1.281/

@inproceedings{ogezi-etal-2023-ualberta, title = "{UA}lberta at {S}em{E}val-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation", author = "Ogezi, Michael and Hauer, Bradley and Omarov, Talgat and Shi, Ning and Kondrak, Grzegorz", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.281", doi = "10.18653/v1/2023.semeval-1.281", pages = "2043--2051", abstract = "We describe the systems of the University of Alberta team for the SemEval-2023 Visual Word Sense Disambiguation (V-WSD) Task. We present a novel algorithm that leverages glosses retrieved from BabelNet, in combination with text and image encoders. Furthermore, we compare language-specific encoders against the application of English encoders to translated texts. As the contexts given in the task datasets are extremely short, we also experiment with augmenting these contexts with descriptions generated by a language model. This yields substantial improvements in accuracy. We describe and evaluate additional V-WSD methods which use image generation and text-conditioned image segmentation. Some of our experimental results exceed those of our official submissions on the test set. Our code is publicly available at \url{https://github.com/UAlberta-NLP/v-wsd}.", }

We describe the systems of the University of Alberta team for the SemEval-2023 Visual Word Sense Disambiguation (V-WSD) Task. We present a novel algorithm that leverages glosses retrieved from BabelNet, in combination with text and image encoders. Furthermore, we compare language-specific encoders against the application of English encoders to translated texts. As the contexts given in the task datasets are extremely short, we also experiment with augmenting these contexts with descriptions generated by a language model. This yields substantial improvements in accuracy. We describe and evaluate additional V-WSD methods which use image generation and text-conditioned image segmentation. Some of our experimental results exceed those of our official submissions on the test set. Our code is publicly available at \url{https://github.com/UAlberta-NLP/v-wsd}.

UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation

semeval-1.281

Poster

2306.14067

https://aclanthology.org/2023.semeval-1.282.bib

https://aclanthology.org/2023.semeval-1.282/

@inproceedings{singh-etal-2023-irel, title = "i{REL} at {S}em{E}val-2023 Task 9: Improving understanding of multilingual Tweets using Translation-Based Augmentation and Domain Adapted Pre-Trained Models", author = "Singh, Bhavyajeet and Maity, Ankita and Kandru, Pavan and Hari, Aditya and Varma, Vasudeva", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.282", doi = "10.18653/v1/2023.semeval-1.282", pages = "2052--2057", abstract = "This paper describes our system (iREL) for Tweet intimacy analysis sharedtask of the SemEval 2023 workshop at ACL 2023. Oursystem achieved an overall Pearson{'}s r score of 0.5924 and ranked 10th on the overall leaderboard. For the unseen languages, we ranked third on the leaderboard and achieved a Pearson{'}s r score of 0.485. We used a single multilingual model for all languages, as discussed in this paper. We provide a detailed description of our pipeline along with multiple ablation experiments to further analyse each component of the pipeline. We demonstrate how translation-based augmentation, domain-specific features, and domain-adapted pre-trained models improve the understanding of intimacy in tweets. The codecan be found at {\textbackslash}href{https://github.com/bhavyajeet/Multilingual-tweet-intimacy}{https://github.com/bhavyajeet/Multilingual-tweet-intimacy}", }

This paper describes our system (iREL) for Tweet intimacy analysis sharedtask of the SemEval 2023 workshop at ACL 2023. Oursystem achieved an overall Pearson{'}s r score of 0.5924 and ranked 10th on the overall leaderboard. For the unseen languages, we ranked third on the leaderboard and achieved a Pearson{'}s r score of 0.485. We used a single multilingual model for all languages, as discussed in this paper. We provide a detailed description of our pipeline along with multiple ablation experiments to further analyse each component of the pipeline. We demonstrate how translation-based augmentation, domain-specific features, and domain-adapted pre-trained models improve the understanding of intimacy in tweets. The codecan be found at {\textbackslash}href{https://github.com/bhavyajeet/Multilingual-tweet-intimacy}{https://github.com/bhavyajeet/Multilingual-tweet-intimacy}

iREL at SemEval-2023 Task 9: Improving understanding of multilingual Tweets using Translation-Based Augmentation and Domain Adapted Pre-Trained Models

semeval-1.282

Poster

https://aclanthology.org/2023.semeval-1.283.bib

https://aclanthology.org/2023.semeval-1.283/

@inproceedings{mohammed-afzal-nakov-2023-team, title = "Team {T}he{S}yllogist at {S}em{E}val-2023 Task 3: Language-Agnostic Framing Detection in Multi-Lingual Online News: A Zero-Shot Transfer Approach", author = "Mohammed Afzal, Osama and Nakov, Preslav", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.283", doi = "10.18653/v1/2023.semeval-1.283", pages = "2058--2061", abstract = "We describe our system for SemEval-2022 Task 3 subtask 2 which on detecting the frames used in a news article in a multi-lingual setup. We propose a multi-lingual approach based on machine translation of the input, followed by an English prediction model. Our system demonstrated good zero-shot transfer capability, achieving micro-F1 scores of 53{\%} for Greek (4th on the leaderboard) and 56.1{\%} for Georgian (3rd on the leaderboard), without any prior training on translated data for these languages. Moreover, our system achieved comparable performance on seven other languages, including German, English, French, Russian, Italian, Polish, and Spanish. Our results demonstrate the feasibility of creating a language-agnostic model for automatic framing detection in online news.", }

We describe our system for SemEval-2022 Task 3 subtask 2 which on detecting the frames used in a news article in a multi-lingual setup. We propose a multi-lingual approach based on machine translation of the input, followed by an English prediction model. Our system demonstrated good zero-shot transfer capability, achieving micro-F1 scores of 53{\%} for Greek (4th on the leaderboard) and 56.1{\%} for Georgian (3rd on the leaderboard), without any prior training on translated data for these languages. Moreover, our system achieved comparable performance on seven other languages, including German, English, French, Russian, Italian, Polish, and Spanish. Our results demonstrate the feasibility of creating a language-agnostic model for automatic framing detection in online news.

Team TheSyllogist at SemEval-2023 Task 3: Language-Agnostic Framing Detection in Multi-Lingual Online News: A Zero-Shot Transfer Approach

semeval-1.283

Poster

https://aclanthology.org/2023.semeval-1.284.bib

https://aclanthology.org/2023.semeval-1.284/

@inproceedings{kandru-etal-2023-tenzin, title = "Tenzin-Gyatso at {S}em{E}val-2023 Task 4: Identifying Human Values behind Arguments Using {D}e{BERT}a", author = "Kandru, Pavan and Singh, Bhavyajeet and Maity, Ankita and Aditya Hari, Kancharla and Varma, Vasudeva", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.284", doi = "10.18653/v1/2023.semeval-1.284", pages = "2062--2066", abstract = "Identifying human values behind arguments isa complex task which requires understandingof premise, stance and conclusion together. Wepropose a method that uses a pre-trained lan-guage model, DeBERTa, to tokenize and con-catenate the text before feeding it into a fullyconnected neural network. We also show thatleveraging the hierarchy in values improves theperformance by .14 F1 score.", }

Identifying human values behind arguments isa complex task which requires understandingof premise, stance and conclusion together. Wepropose a method that uses a pre-trained lan-guage model, DeBERTa, to tokenize and con-catenate the text before feeding it into a fullyconnected neural network. We also show thatleveraging the hierarchy in values improves theperformance by .14 F1 score.

Tenzin-Gyatso at SemEval-2023 Task 4: Identifying Human Values behind Arguments Using DeBERTa

semeval-1.284

Poster

https://aclanthology.org/2023.semeval-1.285.bib

https://aclanthology.org/2023.semeval-1.285/

@inproceedings{cercas-curry-etal-2023-milanlp, title = "{M}ila{NLP} at {S}em{E}val-2023 Task 10: Ensembling Domain-Adapted and Regularized Pretrained Language Models for Robust Sexism Detection", author = "Cercas Curry, Amanda and Attanasio, Giuseppe and Nozza, Debora and Hovy, Dirk", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.285", doi = "10.18653/v1/2023.semeval-1.285", pages = "2067--2074", abstract = "We present the system proposed by the MilaNLP team for the Explainable Detection of Online Sexism (EDOS) shared task. We propose an ensemble modeling approach to combine different classifiers trained with domain adaptation objectives and standard fine-tuning. Our results show that the ensemble is more robust than individual models and that regularized models generate more {``}conservative{''} predictions, mitigating the effects of lexical overfitting.However, our error analysis also finds that many of the misclassified instances are debatable, raising questions about the objective annotatability of hate speech data.", }

We present the system proposed by the MilaNLP team for the Explainable Detection of Online Sexism (EDOS) shared task. We propose an ensemble modeling approach to combine different classifiers trained with domain adaptation objectives and standard fine-tuning. Our results show that the ensemble is more robust than individual models and that regularized models generate more {``}conservative{''} predictions, mitigating the effects of lexical overfitting.However, our error analysis also finds that many of the misclassified instances are debatable, raising questions about the objective annotatability of hate speech data.

MilaNLP at SemEval-2023 Task 10: Ensembling Domain-Adapted and Regularized Pretrained Language Models for Robust Sexism Detection

semeval-1.285

Poster

https://aclanthology.org/2023.semeval-1.286.bib

https://aclanthology.org/2023.semeval-1.286/

@inproceedings{chen-etal-2023-ynu, title = "{YNU}-{HPCC} at {S}em{E}val-2023 Task 6: {LEGAL}-{BERT} Based Hierarchical {B}i{LSTM} with {CRF} for Rhetorical Roles Prediction", author = "Chen, Yu and Zhang, You and Wang, Jin and Zhang, Xuejie", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.286", doi = "10.18653/v1/2023.semeval-1.286", pages = "2075--2081", abstract = "To understand a legal document for real-world applications, SemEval-2023 Task 6 proposes a shared Subtask A, rhetorical roles (RRs) prediction, which requires a system to automatically assign a RR label for each semantical segment in a legal text. In this paper, we propose a LEGAL-BERT based hierarchical BiLSTM model with conditional random field (CRF) for RR prediction, which primarily consists of two parts: word-level and sentence-level encoders. The word-level encoder first adopts a legal-domain pre-trained language model, LEGAL-BERT, initially word-embedding words in each sentence in a document and a word-level BiLSTM further encoding such sentence representation. The sentence-level encoder then uses an attentive pooling method for sentence embedding and a sentence-level BiLSTM for document modeling. Finally, a CRF is utilized to predict RRs for each sentence. The officially released results show that our method outperformed the baseline systems. Our team won 7th rank out of 27 participants in Subtask A.", }

To understand a legal document for real-world applications, SemEval-2023 Task 6 proposes a shared Subtask A, rhetorical roles (RRs) prediction, which requires a system to automatically assign a RR label for each semantical segment in a legal text. In this paper, we propose a LEGAL-BERT based hierarchical BiLSTM model with conditional random field (CRF) for RR prediction, which primarily consists of two parts: word-level and sentence-level encoders. The word-level encoder first adopts a legal-domain pre-trained language model, LEGAL-BERT, initially word-embedding words in each sentence in a document and a word-level BiLSTM further encoding such sentence representation. The sentence-level encoder then uses an attentive pooling method for sentence embedding and a sentence-level BiLSTM for document modeling. Finally, a CRF is utilized to predict RRs for each sentence. The officially released results show that our method outperformed the baseline systems. Our team won 7th rank out of 27 participants in Subtask A.

YNU-HPCC at SemEval-2023 Task 6: LEGAL-BERT Based Hierarchical BiLSTM with CRF for Rhetorical Roles Prediction

semeval-1.286

Poster

https://aclanthology.org/2023.semeval-1.287.bib

https://aclanthology.org/2023.semeval-1.287/

@inproceedings{zhong-etal-2023-uirisc, title = "{UIRISC} at {S}em{E}val-2023 Task 10: Explainable Detection of Online Sexism by Ensembling Fine-tuning Language Models", author = "Zhong, Tianyun and Song, Runhui and Liu, Xunyuan and Wang, Juelin and Wang, Boya and Li, Binyang", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.287", doi = "10.18653/v1/2023.semeval-1.287", pages = "2082--2090", abstract = "Under the umbrella of anonymous social networks, many women have suffered from abuse, discrimination, and other sexist expressions online. However, exsiting methods based on keyword filtering and matching performed poorly on online sexism detection, which lacked the capability to identify implicit stereotypes and discrimination. Therefore, this paper proposes a System of Ensembling Fine-tuning Models (SEFM) at SemEval-2023 Task 10: Explainable Detection of Online Sexism. We firstly use four task-adaptive pre-trained language models to flag all texts. Secondly, we alleviate the data imbalance from two perspectives: over-sampling the labelled data and adjusting the loss function. Thirdly, we add indicators and feedback modules to enhance the overall performance. Our system attained macro F1 scores of 0.8538, 0.6619, and 0.4641 for Subtask A, B, and C, respectively. Our system exhibited strong performance across multiple tasks, with particularly noteworthy performance in Subtask B. Comparison experiments and ablation studies demonstrate the effectiveness of our system.", }

Under the umbrella of anonymous social networks, many women have suffered from abuse, discrimination, and other sexist expressions online. However, exsiting methods based on keyword filtering and matching performed poorly on online sexism detection, which lacked the capability to identify implicit stereotypes and discrimination. Therefore, this paper proposes a System of Ensembling Fine-tuning Models (SEFM) at SemEval-2023 Task 10: Explainable Detection of Online Sexism. We firstly use four task-adaptive pre-trained language models to flag all texts. Secondly, we alleviate the data imbalance from two perspectives: over-sampling the labelled data and adjusting the loss function. Thirdly, we add indicators and feedback modules to enhance the overall performance. Our system attained macro F1 scores of 0.8538, 0.6619, and 0.4641 for Subtask A, B, and C, respectively. Our system exhibited strong performance across multiple tasks, with particularly noteworthy performance in Subtask B. Comparison experiments and ablation studies demonstrate the effectiveness of our system.

UIRISC at SemEval-2023 Task 10: Explainable Detection of Online Sexism by Ensembling Fine-tuning Language Models

semeval-1.287

Poster

https://aclanthology.org/2023.semeval-1.288.bib

https://aclanthology.org/2023.semeval-1.288/

@inproceedings{sultana-etal-2023-csecu, title = "{CSECU}-{DSG} at {S}em{E}val-2023 Task 10: Exploiting Transformers with Stacked {LSTM} for the Explainable Detection of Online Sexism", author = "Sultana, Afrin and Tasnia, Radiathun and Ayman, Nabila and Chy, Abu Nowshed", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.288", doi = "10.18653/v1/2023.semeval-1.288", pages = "2091--2099", abstract = "Sexism is a harmful phenomenon that provokes gender inequalities and social imbalances. The expanding application of sexist content on social media platforms creates an unwelcoming and discomforting environment for many users. The implication of sexism is a multi-faceted subject as it can be integrated with other categories of discrimination. Binary classification tools are frequently employed to identify sexist content, but most of them provide extensive, generic categories with no further insights. SemEval-2023 introduced the Explainable Detection of Online Sexism (EDOS) task that emphasizes detecting and explaining the category of sexist content. The content of this paper details our involvement in this task where we present a neural network architecture employing document embeddings from a fine-tuned transformer-based model into stacked long short-term memory (LSTM) and a fully connected linear (FCL) layer . Our proposed methodology obtained an F1 score of 0.8218 (ranked 51st) in Task A. It achieved an F1 score of 0.5986 (ranked 40th) and 0.4419 (ranked 28th) in Tasks B and C, respectively.", }

Sexism is a harmful phenomenon that provokes gender inequalities and social imbalances. The expanding application of sexist content on social media platforms creates an unwelcoming and discomforting environment for many users. The implication of sexism is a multi-faceted subject as it can be integrated with other categories of discrimination. Binary classification tools are frequently employed to identify sexist content, but most of them provide extensive, generic categories with no further insights. SemEval-2023 introduced the Explainable Detection of Online Sexism (EDOS) task that emphasizes detecting and explaining the category of sexist content. The content of this paper details our involvement in this task where we present a neural network architecture employing document embeddings from a fine-tuned transformer-based model into stacked long short-term memory (LSTM) and a fully connected linear (FCL) layer . Our proposed methodology obtained an F1 score of 0.8218 (ranked 51st) in Task A. It achieved an F1 score of 0.5986 (ranked 40th) and 0.4419 (ranked 28th) in Tasks B and C, respectively.

CSECU-DSG at SemEval-2023 Task 10: Exploiting Transformers with Stacked LSTM for the Explainable Detection of Online Sexism

semeval-1.288

Poster

https://aclanthology.org/2023.semeval-1.289.bib

https://aclanthology.org/2023.semeval-1.289/

@inproceedings{shmalts-2023-john, title = "John Boy Walton at {S}em{E}val-2023 Task 5: An Ensemble Approach to Spoiler Classification and Retrieval for Clickbait Spoiling", author = "Shmalts, Maksim", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.289", doi = "10.18653/v1/2023.semeval-1.289", pages = "2100--2106", abstract = "Clickbait spoiling is a task of generating or retrieving a fairly short text with a purpose to satisfy curiosity of a content consumer without their addressing to the document linked to a clickbait post or headline. In this paper we introduce an ensemble approach to clickbait spoiling task at SemEval-2023. The tasks consists of spoiler classification and retrieval on Webis-Clickbait-22 dataset. We show that such an ensemble solution is quite successful at classification, whereas it might perform poorly at retrieval with no additional features. In conclusion we outline our thoughts on possible directions to improving the approach and shape a set of suggestions to the said features.", }

Clickbait spoiling is a task of generating or retrieving a fairly short text with a purpose to satisfy curiosity of a content consumer without their addressing to the document linked to a clickbait post or headline. In this paper we introduce an ensemble approach to clickbait spoiling task at SemEval-2023. The tasks consists of spoiler classification and retrieval on Webis-Clickbait-22 dataset. We show that such an ensemble solution is quite successful at classification, whereas it might perform poorly at retrieval with no additional features. In conclusion we outline our thoughts on possible directions to improving the approach and shape a set of suggestions to the said features.

John Boy Walton at SemEval-2023 Task 5: An Ensemble Approach to Spoiler Classification and Retrieval for Clickbait Spoiling

semeval-1.289

Poster

https://aclanthology.org/2023.semeval-1.290.bib

https://aclanthology.org/2023.semeval-1.290/

@inproceedings{stefanovitch-etal-2023-teamec, title = "{T}eam{EC} at {S}em{E}val-2023 Task 4: Transformers vs. Low-Resource Dictionaries, Expert Dictionary vs. Learned Dictionary", author = "Stefanovitch, Nicolas and De Longueville, Bertrand and Scharfbillig, Mario", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.290", doi = "10.18653/v1/2023.semeval-1.290", pages = "2107--2111", abstract = "This paper describes the system we used to participate in the shared task, as well as additional experiments beyond the scope of the shared task, but using its data. Our primary goal is to compare the effectiveness of transformers model compared to low-resource dictionaries. Secondly, we compare the difference in performance of a learned dictionary and of a dictionary designed by experts in the field of values. Our findings surprisingly show that transformers perform on par with a dictionary containing less than 1k words, when evaluated with 19 fine-grained categories, and only outperform a dictionary-based approach in a coarse setting with 10 categories. Interestingly, the expert dictionary has a precision on par with the learned one, while its recall is clearly lower, potentially an indication of overfitting of topics to values in the shared task{'}s dataset. Our findings should be of interest to both the NLP and Value scientific communities on the use of automated approaches for value classification", }

This paper describes the system we used to participate in the shared task, as well as additional experiments beyond the scope of the shared task, but using its data. Our primary goal is to compare the effectiveness of transformers model compared to low-resource dictionaries. Secondly, we compare the difference in performance of a learned dictionary and of a dictionary designed by experts in the field of values. Our findings surprisingly show that transformers perform on par with a dictionary containing less than 1k words, when evaluated with 19 fine-grained categories, and only outperform a dictionary-based approach in a coarse setting with 10 categories. Interestingly, the expert dictionary has a precision on par with the learned one, while its recall is clearly lower, potentially an indication of overfitting of topics to values in the shared task{'}s dataset. Our findings should be of interest to both the NLP and Value scientific communities on the use of automated approaches for value classification

TeamEC at SemEval-2023 Task 4: Transformers vs. Low-Resource Dictionaries, Expert Dictionary vs. Learned Dictionary

semeval-1.290

Poster

https://aclanthology.org/2023.semeval-1.291.bib

https://aclanthology.org/2023.semeval-1.291/

@inproceedings{tasneem-etal-2023-csecu, title = "{CSECU}-{DSG} at {S}em{E}val-2023 Task 6: Segmenting Legal Documents into Rhetorical Roles via Fine-tuned Transformer Architecture", author = "Tasneem, Fareen and Hossain, Tashin and Naim, Jannatun and Chy, Abu Nowshed", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.291", doi = "10.18653/v1/2023.semeval-1.291", pages = "2112--2117", abstract = "Automated processing of legal documents is essential to manage the enormous volume of legal corpus and to make it easily accessible to a broad spectrum of people. But due to the amorphous and variable nature of legal documents, it is very challenging to directly proceed with complicated processes such as summarization, analysis, and query. Segmenting the documents as per the rhetorical roles can aid and accelerate such procedures. This paper describes our participation in SemEval-2023 task 6: Sub-task A: Rhetorical Roles Prediction. We utilize a finetuned Legal-BERT to address this task. We also conduct an error analysis to illustrate the shortcomings of our deployed approach.", }

Automated processing of legal documents is essential to manage the enormous volume of legal corpus and to make it easily accessible to a broad spectrum of people. But due to the amorphous and variable nature of legal documents, it is very challenging to directly proceed with complicated processes such as summarization, analysis, and query. Segmenting the documents as per the rhetorical roles can aid and accelerate such procedures. This paper describes our participation in SemEval-2023 task 6: Sub-task A: Rhetorical Roles Prediction. We utilize a finetuned Legal-BERT to address this task. We also conduct an error analysis to illustrate the shortcomings of our deployed approach.

CSECU-DSG at SemEval-2023 Task 6: Segmenting Legal Documents into Rhetorical Roles via Fine-tuned Transformer Architecture

semeval-1.291

Poster

https://aclanthology.org/2023.semeval-1.292.bib

https://aclanthology.org/2023.semeval-1.292/

@inproceedings{karimi-flek-2023-caisa, title = "{CAISA} at {S}em{E}val-2023 Task 8: Counterfactual Data Augmentation for Mitigating Class Imbalance in Causal Claim Identification", author = "Karimi, Akbar and Flek, Lucie", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.292", doi = "10.18653/v1/2023.semeval-1.292", pages = "2118--2123", abstract = "Class imbalance problem can cause machine learning models to produce an undesirable performance on the minority class as well as the whole dataset. Using data augmentation techniques to increase the number of samples is one way to tackle this problem. We introduce a novel counterfactual data augmentation by verb replacement for the identification of medical claims. In addition, we investigate the impact of this method and compare it with 3 other data augmentation techniques, showing that the proposed method can result in significant (relative) improvement on the minority class.", }

Class imbalance problem can cause machine learning models to produce an undesirable performance on the minority class as well as the whole dataset. Using data augmentation techniques to increase the number of samples is one way to tackle this problem. We introduce a novel counterfactual data augmentation by verb replacement for the identification of medical claims. In addition, we investigate the impact of this method and compare it with 3 other data augmentation techniques, showing that the proposed method can result in significant (relative) improvement on the minority class.

CAISA at SemEval-2023 Task 8: Counterfactual Data Augmentation for Mitigating Class Imbalance in Causal Claim Identification

semeval-1.292

Poster

2306.00346

https://aclanthology.org/2023.semeval-1.293.bib

https://aclanthology.org/2023.semeval-1.293/

@inproceedings{qachfar-verma-2023-redaspersuasion, title = "{R}e{DASP}ersuasion at {S}em{E}val-2023 Task 3: Persuasion Detection using Multilingual Transformers and Language Agnostic Features", author = "Qachfar, Fatima Zahra and Verma, Rakesh", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.293", doi = "10.18653/v1/2023.semeval-1.293", pages = "2124--2132", abstract = "This paper describes a multilingual persuasion detection system that incorporates persuasion technique attributes for a multi-label classification task. The proposed method has two advantages. First, it combines persuasion features with a sequence classification transformer to classify persuasion techniques. Second, it is a language agnostic approach that supports a total of 100 languages, guaranteed by the multilingual transformer module and the Google translator interface. We found that our persuasion system outperformed the SemEval baseline in all languages except zero shot prediction languages, which did not constitute the main focus of our research. With the highest F1-Micro score of 0.45, Italian achieved the eighth position on the leaderboard.", }

This paper describes a multilingual persuasion detection system that incorporates persuasion technique attributes for a multi-label classification task. The proposed method has two advantages. First, it combines persuasion features with a sequence classification transformer to classify persuasion techniques. Second, it is a language agnostic approach that supports a total of 100 languages, guaranteed by the multilingual transformer module and the Google translator interface. We found that our persuasion system outperformed the SemEval baseline in all languages except zero shot prediction languages, which did not constitute the main focus of our research. With the highest F1-Micro score of 0.45, Italian achieved the eighth position on the leaderboard.

ReDASPersuasion at SemEval-2023 Task 3: Persuasion Detection using Multilingual Transformers and Language Agnostic Features

semeval-1.293

Poster

https://aclanthology.org/2023.semeval-1.294.bib

https://aclanthology.org/2023.semeval-1.294/

@inproceedings{maity-etal-2023-irel, title = "{IREL} at {S}em{E}val-2023 Task 11: User Conditioned Modelling for Toxicity Detection in Subjective Tasks", author = "Maity, Ankita and Kandru, Pavan and Singh, Bhavyajeet and Aditya Hari, Kancharla and Varma, Vasudeva", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.294", doi = "10.18653/v1/2023.semeval-1.294", pages = "2133--2136", abstract = "This paper describes our system used in the SemEval-2023 Task 11 Learning With Disagreements (Le-Wi-Di). This is a subjective task since it deals with detecting hate speech, misogyny and offensive language. Thus, disagreement among annotators is expected. We experiment with different settings like loss functions specific for subjective tasks and include anonymized annotator-specific information to help us understand the level of disagreement. We perform an in-depth analysis of the performance discrepancy of these different modelling choices. Our system achieves a cross-entropy of 0.58, 4.01 and 3.70 on the test sets of HS-Brexit, ArMIS and MD-Agreement, respectively. Our code implementation is publicly available.", }

This paper describes our system used in the SemEval-2023 Task 11 Learning With Disagreements (Le-Wi-Di). This is a subjective task since it deals with detecting hate speech, misogyny and offensive language. Thus, disagreement among annotators is expected. We experiment with different settings like loss functions specific for subjective tasks and include anonymized annotator-specific information to help us understand the level of disagreement. We perform an in-depth analysis of the performance discrepancy of these different modelling choices. Our system achieves a cross-entropy of 0.58, 4.01 and 3.70 on the test sets of HS-Brexit, ArMIS and MD-Agreement, respectively. Our code implementation is publicly available.

IREL at SemEval-2023 Task 11: User Conditioned Modelling for Toxicity Detection in Subjective Tasks

semeval-1.294

Poster

https://aclanthology.org/2023.semeval-1.295.bib

https://aclanthology.org/2023.semeval-1.295/

@inproceedings{kohli-tiwari-2023-arguably, title = "Arguably at {S}em{E}val-2023 Task 11: Learning the disagreements using unsupervised behavioral clustering and language models", author = "Kohli, Guneet Singh and Tiwari, Vinayak", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.295", doi = "10.18653/v1/2023.semeval-1.295", pages = "2137--2142", abstract = "We describe SemEval-2023 Task 11 on behavioral segregation of annotations to find the similarities and contextual thinking of a group of annotators. We have utilized a behavioral segmentation analysis on the annotators to model them independently and combine the results to yield soft and hard scores. Our team focused on experimenting with hierarchical clustering with various distance metrics for similarity, dissimilarity, and reliability. We modeled the clusters and assigned weightage to find the soft and hard scores. Our team was able to find out hidden behavioral patterns among the judgments of annotators after rigorous experiments. The proposed system is made available.", }

We describe SemEval-2023 Task 11 on behavioral segregation of annotations to find the similarities and contextual thinking of a group of annotators. We have utilized a behavioral segmentation analysis on the annotators to model them independently and combine the results to yield soft and hard scores. Our team focused on experimenting with hierarchical clustering with various distance metrics for similarity, dissimilarity, and reliability. We modeled the clusters and assigned weightage to find the soft and hard scores. Our team was able to find out hidden behavioral patterns among the judgments of annotators after rigorous experiments. The proposed system is made available.

Arguably at SemEval-2023 Task 11: Learning the disagreements using unsupervised behavioral clustering and language models

semeval-1.295

Poster

https://aclanthology.org/2023.semeval-1.296.bib

https://aclanthology.org/2023.semeval-1.296/

@inproceedings{ramachandran-etal-2023-masonnlp, title = "{M}ason{NLP}+ at {S}em{E}val-2023 Task 8: Extracting Medical Questions, Experiences and Claims from Social Media using Knowledge-Augmented Pre-trained Language Models", author = "Ramachandran, Giridhar Kaushik and Gangavarapu, Haritha and Lybarger, Kevin and Uzuner, Ozlem", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.296", doi = "10.18653/v1/2023.semeval-1.296", pages = "2143--2152", abstract = "In online forums like Reddit, users share their experiences with medical conditions and treatments, including making claims, asking questions, and discussing the effects of treatments on their health. Building systems to understand this information can effectively monitor the spread of misinformation and verify user claims. The Task-8 of the 2023 International Workshop on Semantic Evaluation focused on medical applications, specifically extracting patient experience- and medical condition-related entities from user posts on social media. The Reddit Health Online Talk (RedHot) corpus contains posts from medical condition-related subreddits with annotations characterizing the patient experience and medical conditions. In Subtask-1, patient experience is characterized by personal experience, questions, and claims. In Subtask-2, medical conditions are characterized by population, intervention, and outcome. For the automatic extraction of patient experiences and medical condition information, as a part of the challenge, we proposed language-model-based extraction systems that ranked {\$}3{\^{}}{rd}{\$} on both subtasks{'} leaderboards. In this work, we describe our approach and, in addition, explore the automatic extraction of this information using domain-specific language models and the inclusion of external knowledge.", }

In online forums like Reddit, users share their experiences with medical conditions and treatments, including making claims, asking questions, and discussing the effects of treatments on their health. Building systems to understand this information can effectively monitor the spread of misinformation and verify user claims. The Task-8 of the 2023 International Workshop on Semantic Evaluation focused on medical applications, specifically extracting patient experience- and medical condition-related entities from user posts on social media. The Reddit Health Online Talk (RedHot) corpus contains posts from medical condition-related subreddits with annotations characterizing the patient experience and medical conditions. In Subtask-1, patient experience is characterized by personal experience, questions, and claims. In Subtask-2, medical conditions are characterized by population, intervention, and outcome. For the automatic extraction of patient experiences and medical condition information, as a part of the challenge, we proposed language-model-based extraction systems that ranked {\$}3{\^{}}{rd}{\$} on both subtasks{'} leaderboards. In this work, we describe our approach and, in addition, explore the automatic extraction of this information using domain-specific language models and the inclusion of external knowledge.

MasonNLP+ at SemEval-2023 Task 8: Extracting Medical Questions, Experiences and Claims from Social Media using Knowledge-Augmented Pre-trained Language Models

semeval-1.296

Poster

2304.13875

https://aclanthology.org/2023.semeval-1.297.bib

https://aclanthology.org/2023.semeval-1.297/

@inproceedings{aryal-prioleau-2023-howard, title = "{H}oward {U}niversity Computer Science at {S}em{E}val-2023 Task 12: A 2-Step System Design for Multilingual Sentiment Classification with Language Identification", author = "Aryal, Saurav and Prioleau, Howard", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.297", doi = "10.18653/v1/2023.semeval-1.297", pages = "2153--2159", abstract = "The recent release of the AfriSenti-SemEval shared Task 12 has made available 14 new datasets annotated for sentiment analysis on African Languages. We proposed and evaluated two approaches to this task, Delta TF-IDF, and a proposed Language-Specific Model Fusion Algorithm using Language Identification, both of which produced comparable or better classification performance than the current state-of-art models on this task: AfriBERTa, AfroXLMR, and AfroLM.", }

The recent release of the AfriSenti-SemEval shared Task 12 has made available 14 new datasets annotated for sentiment analysis on African Languages. We proposed and evaluated two approaches to this task, Delta TF-IDF, and a proposed Language-Specific Model Fusion Algorithm using Language Identification, both of which produced comparable or better classification performance than the current state-of-art models on this task: AfriBERTa, AfroXLMR, and AfroLM.

Howard University Computer Science at SemEval-2023 Task 12: A 2-Step System Design for Multilingual Sentiment Classification with Language Identification

semeval-1.297

Poster

https://aclanthology.org/2023.semeval-1.298.bib

https://aclanthology.org/2023.semeval-1.298/

@inproceedings{ghahroodi-etal-2023-sut, title = "{SUT} at {S}em{E}val-2023 Task 1: Prompt Generation for Visual Word Sense Disambiguation", author = "Ghahroodi, Omid and Dalili, Seyed Arshan and Mesforoush, Sahel and Asgari, Ehsaneddin", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.298", doi = "10.18653/v1/2023.semeval-1.298", pages = "2160--2163", abstract = "Visual Word Sense Disambiguation (V-WSD) identifies the correct visual sense of a multi-sense word in a specific context. This can be challenging as images may need to provide additional context and words may have multiple senses. A proper V-WSD system can benefit applications like image retrieval and captioning. This paper proposes a Prompt Generation approach to solve this challenge. This approach improves the robustness of language-image models like CLIP to contextual ambiguities and helps them better correlate between textual and visual contexts of different senses of words.", }

Visual Word Sense Disambiguation (V-WSD) identifies the correct visual sense of a multi-sense word in a specific context. This can be challenging as images may need to provide additional context and words may have multiple senses. A proper V-WSD system can benefit applications like image retrieval and captioning. This paper proposes a Prompt Generation approach to solve this challenge. This approach improves the robustness of language-image models like CLIP to contextual ambiguities and helps them better correlate between textual and visual contexts of different senses of words.

SUT at SemEval-2023 Task 1: Prompt Generation for Visual Word Sense Disambiguation

semeval-1.298

Poster

https://aclanthology.org/2023.semeval-1.299.bib

https://aclanthology.org/2023.semeval-1.299/

@inproceedings{ghahroodi-etal-2023-sina, title = "Sina at {S}em{E}val-2023 Task 4: A Class-Token Attention-based Model for Human Value Detection", author = "Ghahroodi, Omid and Sadraei Javaheri, Mohammad Ali and Dastgheib, Doratossadat and Soleymani Baghshah, Mahdieh and Rohban, Mohammad Hossein and Rabiee, Hamid and Asgari, Ehsaneddin", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.299", doi = "10.18653/v1/2023.semeval-1.299", pages = "2164--2167", abstract = "The human values expressed in argumentative texts can provide valuable insights into the culture of a society. They can be helpful in various applications such as value-based profiling and ethical analysis. However, one of the first steps in achieving this goal is to detect the category of human value from an argument accurately. This task is challenging due to the lack of data and the need for philosophical inference. It also can be challenging for humans to classify arguments according to their underlying human values. This paper elaborates on our model for the SemEval 2023 Task 4 on human value detection. We propose a class-token attention-based model and evaluate it against baseline models, including finetuned BERT language model and a keyword-based approach.", }

The human values expressed in argumentative texts can provide valuable insights into the culture of a society. They can be helpful in various applications such as value-based profiling and ethical analysis. However, one of the first steps in achieving this goal is to detect the category of human value from an argument accurately. This task is challenging due to the lack of data and the need for philosophical inference. It also can be challenging for humans to classify arguments according to their underlying human values. This paper elaborates on our model for the SemEval 2023 Task 4 on human value detection. We propose a class-token attention-based model and evaluate it against baseline models, including finetuned BERT language model and a keyword-based approach.

Sina at SemEval-2023 Task 4: A Class-Token Attention-based Model for Human Value Detection

semeval-1.299

Poster

https://aclanthology.org/2023.semeval-1.300.bib

https://aclanthology.org/2023.semeval-1.300/

@inproceedings{sadeghi-etal-2023-sinaai, title = "{S}ina{AI} at {S}em{E}val-2023 Task 3: A Multilingual Transformer Language Model-based Approach for the Detection of News Genre, Framing and Persuasion Techniques", author = "Sadeghi, Aryan and Alipour, Reza and Taeb, Kamyar and Morassafar, Parimehr and Salemahim, Nima and Asgari, Ehsaneddin", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.300", doi = "10.18653/v1/2023.semeval-1.300", pages = "2168--2173", abstract = "This paper describes SinaAI{'}s participation in SemEval-2023 Task 3, which involves detecting propaganda in news articles across multiple languages. The task comprises three sub-tasks: (i) genre detection, (ii) news framing,and (iii) persuasion technique identification. The employed dataset includes news articles in nine languages and domains, including English, French, Italian, German, Polish, Russian, Georgian, Greek, and Spanish, with labeled instances of news framing, genre, and persuasion techniques. Our approach combines fine-tuning multilingual language models such as XLM, LaBSE, and mBERT with data augmentation techniques. Our experimental results show that XLM outperforms other models in terms of F1-Micro in and F1-Macro, and the ensemble of XLM and LaBSE achieved the best performance. Our study highlights the effectiveness of multilingual sentence embedding models in multilingual propaganda detection. Our models achieved highest score for two languages (greek and italy) in sub-task 1 and one language (Russian) for sub-task 2.", }

This paper describes SinaAI{'}s participation in SemEval-2023 Task 3, which involves detecting propaganda in news articles across multiple languages. The task comprises three sub-tasks: (i) genre detection, (ii) news framing,and (iii) persuasion technique identification. The employed dataset includes news articles in nine languages and domains, including English, French, Italian, German, Polish, Russian, Georgian, Greek, and Spanish, with labeled instances of news framing, genre, and persuasion techniques. Our approach combines fine-tuning multilingual language models such as XLM, LaBSE, and mBERT with data augmentation techniques. Our experimental results show that XLM outperforms other models in terms of F1-Micro in and F1-Macro, and the ensemble of XLM and LaBSE achieved the best performance. Our study highlights the effectiveness of multilingual sentence embedding models in multilingual propaganda detection. Our models achieved highest score for two languages (greek and italy) in sub-task 1 and one language (Russian) for sub-task 2.

SinaAI at SemEval-2023 Task 3: A Multilingual Transformer Language Model-based Approach for the Detection of News Genre, Framing and Persuasion Techniques

semeval-1.300

Poster

https://aclanthology.org/2023.semeval-1.301.bib

https://aclanthology.org/2023.semeval-1.301/

@inproceedings{mijatovic-etal-2023-rcln, title = "{RCLN} at {S}em{E}val-2023 Task 1: Leveraging Stable Diffusion and Image Captions for Visual {WSD}", author = "Mijatovic, Antonina and Buscaldi, Davide and Borisova, Ekaterina", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.301", doi = "10.18653/v1/2023.semeval-1.301", pages = "2174--2178", abstract = "This paper describes the participation of the RCLN team at the Visual Word Sense Disambiguation task at SemEval 2023. The participation was focused on the use of CLIP as a base model for the matching between text and images with additional information coming from captions generated from images and the generation of images from the prompt text using Stable Diffusion. The results we obtained are not particularly good, but interestingly enough, we were able to improve over the CLIP baseline in Italian by recurring simply to the generated images.", }

This paper describes the participation of the RCLN team at the Visual Word Sense Disambiguation task at SemEval 2023. The participation was focused on the use of CLIP as a base model for the matching between text and images with additional information coming from captions generated from images and the generation of images from the prompt text using Stable Diffusion. The results we obtained are not particularly good, but interestingly enough, we were able to improve over the CLIP baseline in Italian by recurring simply to the generated images.

RCLN at SemEval-2023 Task 1: Leveraging Stable Diffusion and Image Captions for Visual WSD

semeval-1.301

Poster

https://aclanthology.org/2023.semeval-1.302.bib

https://aclanthology.org/2023.semeval-1.302/

@inproceedings{mohammed-etal-2023-friedrich, title = "Friedrich Nietzsche at {S}em{E}val-2023 Task 4: Detection of Human Values from Text Using Machine Learning", author = "Mohammed, Abdul Jawad and Sundharram, Sruthi and Sharma, Sanidhya", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.302", doi = "10.18653/v1/2023.semeval-1.302", pages = "2179--2183", abstract = "Literature permeates through almost every facet of our lives, whether through books, magazines, or internet articles. Moreover, every piece of written work contains ideas and opinions that we tend to relate to, accept or disregard, debate over, or enlighten ourselves with. However, the existence of subtle themes that are difficult to discern had inspired us to utilize four machine learning algorithms: Decision Trees, Random Forest, Logistic Regression, and Support Vec- tor Classifier (SVC) to aid in their detection. Trained on the ValueEval data set as a multi- label classification problem, the supervised ma- chine learning models did not perform as well as expected, with F1 metrics hovering from 0.0 to 0.04 for each value. Noting this, the lim- itations and weaknesses of our approach are discussed in our paper.", }

Literature permeates through almost every facet of our lives, whether through books, magazines, or internet articles. Moreover, every piece of written work contains ideas and opinions that we tend to relate to, accept or disregard, debate over, or enlighten ourselves with. However, the existence of subtle themes that are difficult to discern had inspired us to utilize four machine learning algorithms: Decision Trees, Random Forest, Logistic Regression, and Support Vec- tor Classifier (SVC) to aid in their detection. Trained on the ValueEval data set as a multi- label classification problem, the supervised ma- chine learning models did not perform as well as expected, with F1 metrics hovering from 0.0 to 0.04 for each value. Noting this, the lim- itations and weaknesses of our approach are discussed in our paper.

Friedrich Nietzsche at SemEval-2023 Task 4: Detection of Human Values from Text Using Machine Learning

semeval-1.302

Poster

https://aclanthology.org/2023.semeval-1.303.bib

https://aclanthology.org/2023.semeval-1.303/

@inproceedings{ahmadi-etal-2023-azaad, title = "azaad@{BND} at {S}em{E}val-2023 Task 2: How to Go from a Simple Transformer Model to a Better Model to Get Better Results in Natural Language Processing", author = "Ahmadi, Reza and Arefi, Shiva and Jafarabad, Mohammad", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.303", doi = "10.18653/v1/2023.semeval-1.303", pages = "2184--2187", abstract = "In this article, which was prepared for the sameval2023 competition (task number 2), information about the implementation techniques of the transformer model and the use of the pre-trained BERT model in order to identify the named entity (NER) in the English language, has been collected and also the implementation method is explained. Finally, it led to an F1 score of about 57{\%} for Fine-grained and 72{\%} for Coarse-grained in the dev data. In the final test data, F1 score reached 50{\%}.", }

In this article, which was prepared for the sameval2023 competition (task number 2), information about the implementation techniques of the transformer model and the use of the pre-trained BERT model in order to identify the named entity (NER) in the English language, has been collected and also the implementation method is explained. Finally, it led to an F1 score of about 57{\%} for Fine-grained and 72{\%} for Coarse-grained in the dev data. In the final test data, F1 score reached 50{\%}.

azaad@BND at SemEval-2023 Task 2: How to Go from a Simple Transformer Model to a Better Model to Get Better Results in Natural Language Processing

semeval-1.303

Poster

https://aclanthology.org/2023.semeval-1.304.bib

https://aclanthology.org/2023.semeval-1.304/

@inproceedings{zhou-2023-pinganlifeinsurance, title = "{P}ing{A}n{L}ife{I}nsurance at {S}em{E}val-2023 Task 10: Using Multi-Task Learning to Better Detect Online Sexism", author = "Zhou, Mengyuan", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.304", doi = "10.18653/v1/2023.semeval-1.304", pages = "2188--2192", abstract = "This paper describes our system used in the SemEval-2023 Task 10: Towards ExplainableDetection of Online Sexism (Kirk et al., 2023). The harmful effects of sexism on the internet have impacted both men and women, yet current research lacks a fine-grained classification of sexist content. The task involves three hierarchical sub-tasks, which we addressed by employing a multitask-learning framework. To further enhance our system{'}s performance, we pre-trained the roberta-large (Liu et al., 2019b) and deberta-v3-large (He et al., 2021) models on two million unlabeled data, resulting in significant improvements on sub-tasks A and C. In addition, the multitask-learning approach boosted the performance of our models on subtasks A and B. Our system exhibits promising results in achieving explainable detection of online sexism, attaining a test f1-score of 0.8746 on sub-task A (ranking 1st on the leaderboard), and ranking 5th on sub-tasks B and C.", }

This paper describes our system used in the SemEval-2023 Task 10: Towards ExplainableDetection of Online Sexism (Kirk et al., 2023). The harmful effects of sexism on the internet have impacted both men and women, yet current research lacks a fine-grained classification of sexist content. The task involves three hierarchical sub-tasks, which we addressed by employing a multitask-learning framework. To further enhance our system{'}s performance, we pre-trained the roberta-large (Liu et al., 2019b) and deberta-v3-large (He et al., 2021) models on two million unlabeled data, resulting in significant improvements on sub-tasks A and C. In addition, the multitask-learning approach boosted the performance of our models on subtasks A and B. Our system exhibits promising results in achieving explainable detection of online sexism, attaining a test f1-score of 0.8746 on sub-task A (ranking 1st on the leaderboard), and ranking 5th on sub-tasks B and C.

PingAnLifeInsurance at SemEval-2023 Task 10: Using Multi-Task Learning to Better Detect Online Sexism

semeval-1.304

Poster

https://aclanthology.org/2023.semeval-1.305.bib

https://aclanthology.org/2023.semeval-1.305/

@inproceedings{kirk-etal-2023-semeval, title = "{S}em{E}val-2023 Task 10: Explainable Detection of Online Sexism", author = {Kirk, Hannah and Yin, Wenjie and Vidgen, Bertie and R{\"o}ttger, Paul}, editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.305", doi = "10.18653/v1/2023.semeval-1.305", pages = "2193--2210", abstract = "Online sexism is a widespread and harmful phenomenon. Automated tools can assist the detection of sexism at scale. Binary detection, however, disregards the diversity of sexist content, and fails to provide clear explanations for why something is sexist. To address this issue, we introduce SemEval Task 10 on the Explainable Detection of Online Sexism (EDOS). We make three main contributions: i) a novel hierarchical taxonomy of sexist content, which includes granular vectors of sexism to aid explainability; ii) a new dataset of 20,000 social media comments with fine-grained labels, along with larger unlabelled datasets for model adaptation; and iii) baseline models as well as an analysis of the methods, results and errors for participant submissions to our task.", }

Online sexism is a widespread and harmful phenomenon. Automated tools can assist the detection of sexism at scale. Binary detection, however, disregards the diversity of sexist content, and fails to provide clear explanations for why something is sexist. To address this issue, we introduce SemEval Task 10 on the Explainable Detection of Online Sexism (EDOS). We make three main contributions: i) a novel hierarchical taxonomy of sexist content, which includes granular vectors of sexism to aid explainability; ii) a new dataset of 20,000 social media comments with fine-grained labels, along with larger unlabelled datasets for model adaptation; and iii) baseline models as well as an analysis of the methods, results and errors for participant submissions to our task.

SemEval-2023 Task 10: Explainable Detection of Online Sexism

semeval-1.305

Poster

2303.04222

https://aclanthology.org/2023.semeval-1.306.bib

https://aclanthology.org/2023.semeval-1.306/

@inproceedings{deturck-etal-2023-ertim, title = "Ertim at {S}em{E}val-2023 Task 2: Fine-tuning of Transformer Language Models and External Knowledge Leveraging for {NER} in {F}arsi, {E}nglish, {F}rench and {C}hinese", author = "Deturck, Kevin and Magistry, Pierre and Diot-Parvaz Ahmad, B{\'e}n{\'e}dicte and Wang, Ilaine and Nouvel, Damien and Lafayette, Hugo", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.306", doi = "10.18653/v1/2023.semeval-1.306", pages = "2211--2215", abstract = "Transformer language models are now a solid baseline for Named Entity Recognition and can be significantly improved by leveraging complementary resources, either by integrating external knowledge or by annotating additional data. In a preliminary step, this work presents experiments on fine-tuning transformer models. Then, a set of experiments has been conducted with a Wikipedia-based reclassification system. Additionally, we conducted a small annotation campaign on the Farsi language to evaluate the impact of additional data. These two methods with complementary resources showed improvements compared to fine-tuning only.", }

Transformer language models are now a solid baseline for Named Entity Recognition and can be significantly improved by leveraging complementary resources, either by integrating external knowledge or by annotating additional data. In a preliminary step, this work presents experiments on fine-tuning transformer models. Then, a set of experiments has been conducted with a Wikipedia-based reclassification system. Additionally, we conducted a small annotation campaign on the Farsi language to evaluate the impact of additional data. These two methods with complementary resources showed improvements compared to fine-tuning only.

Ertim at SemEval-2023 Task 2: Fine-tuning of Transformer Language Models and External Knowledge Leveraging for NER in Farsi, English, French and Chinese

semeval-1.306

Poster

https://aclanthology.org/2023.semeval-1.307.bib

https://aclanthology.org/2023.semeval-1.307/

@inproceedings{jullien-etal-2023-semeval, title = "{S}em{E}val-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", author = {Jullien, Ma{\"e}l and Valentino, Marco and Frost, Hannah and O{'}regan, Paul and Landers, Donal and Freitas, Andr{\'e}}, editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.307", doi = "10.18653/v1/2023.semeval-1.307", pages = "2216--2226", abstract = "This paper describes the results of SemEval 2023 task 7 {--} Multi-Evidence Natural Language Inference for Clinical Trial Data (NLI4CT) {--} consisting of 2 tasks, a Natural Language Inference (NLI) task, and an evidence selection task on clinical trial data. The proposed challenges require multi-hop biomedical and numerical reasoning, which are of significant importance to the development of systems capable of large-scale interpretation and retrieval of medical evidence, to provide personalized evidence-based care. Task 1, the entailment task, received 643 submissions from 40 participants, and Task 2, the evidence selection task, received 364 submissions from 23 participants. The tasks are challenging, with the majority of submitted systems failing to significantly outperform the majority class baseline on the entailment task, and we observe significantly better performance on the evidence selection task than on the entailment task. Increasing the number of model parameters leads to a direct increase in performance, far more significant than the effect of biomedical pre-training. Future works could explore the limitations of large models for generalization and numerical inference, and investigate methods to augment clinical datasets to allow for more rigorous testing and to facilitate fine-tuning. We envisage that the dataset, models, and results of this task will be useful to the biomedical NLI and evidence retrieval communities. The dataset, competition leaderboard, and website are publicly available.", }

This paper describes the results of SemEval 2023 task 7 {--} Multi-Evidence Natural Language Inference for Clinical Trial Data (NLI4CT) {--} consisting of 2 tasks, a Natural Language Inference (NLI) task, and an evidence selection task on clinical trial data. The proposed challenges require multi-hop biomedical and numerical reasoning, which are of significant importance to the development of systems capable of large-scale interpretation and retrieval of medical evidence, to provide personalized evidence-based care. Task 1, the entailment task, received 643 submissions from 40 participants, and Task 2, the evidence selection task, received 364 submissions from 23 participants. The tasks are challenging, with the majority of submitted systems failing to significantly outperform the majority class baseline on the entailment task, and we observe significantly better performance on the evidence selection task than on the entailment task. Increasing the number of model parameters leads to a direct increase in performance, far more significant than the effect of biomedical pre-training. Future works could explore the limitations of large models for generalization and numerical inference, and investigate methods to augment clinical datasets to allow for more rigorous testing and to facilitate fine-tuning. We envisage that the dataset, models, and results of this task will be useful to the biomedical NLI and evidence retrieval communities. The dataset, competition leaderboard, and website are publicly available.

SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data

semeval-1.307

Poster

2305.02993

https://aclanthology.org/2023.semeval-1.308.bib

https://aclanthology.org/2023.semeval-1.308/

@inproceedings{raganato-etal-2023-semeval, title = "{S}em{E}val-2023 Task 1: Visual Word Sense Disambiguation", author = "Raganato, Alessandro and Calixto, Iacer and Ushio, Asahi and Camacho-Collados, Jose and Pilehvar, Mohammad Taher", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.308", doi = "10.18653/v1/2023.semeval-1.308", pages = "2227--2234", abstract = "This paper presents the Visual Word Sense Disambiguation (Visual-WSD) task. The objective of Visual-WSD is to identify among a set of ten images the one that corresponds to the intended meaning of a given ambiguous word which is accompanied with minimal context. The task provides datasets for three different languages: English, Italian, and Farsi.We received a total of 96 different submissions. Out of these, 40 systems outperformed a strong zero-shot CLIP-based baseline. Participating systems proposed different zero- and few-shot approaches, often involving generative models and data augmentation. More information can be found on the task{'}s website: {\textbackslash}url{https://raganato.github.io/vwsd/}.", }

This paper presents the Visual Word Sense Disambiguation (Visual-WSD) task. The objective of Visual-WSD is to identify among a set of ten images the one that corresponds to the intended meaning of a given ambiguous word which is accompanied with minimal context. The task provides datasets for three different languages: English, Italian, and Farsi.We received a total of 96 different submissions. Out of these, 40 systems outperformed a strong zero-shot CLIP-based baseline. Participating systems proposed different zero- and few-shot approaches, often involving generative models and data augmentation. More information can be found on the task{'}s website: {\textbackslash}url{https://raganato.github.io/vwsd/}.

SemEval-2023 Task 1: Visual Word Sense Disambiguation

semeval-1.308

Poster

https://aclanthology.org/2023.semeval-1.309.bib

https://aclanthology.org/2023.semeval-1.309/

@inproceedings{pei-etal-2023-semeval, title = "{S}em{E}val-2023 Task 9: Multilingual Tweet Intimacy Analysis", author = "Pei, Jiaxin and Silva, V{\'\i}tor and Bos, Maarten and Liu, Yozen and Neves, Leonardo and Jurgens, David and Barbieri, Francesco", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.309", doi = "10.18653/v1/2023.semeval-1.309", pages = "2235--2246", abstract = "Intimacy is an important social aspect of language. Computational modeling of intimacy in language could help many downstream applications like dialogue systems and offensiveness detection. Despite its importance, resources and approaches on modeling textual intimacy remain rare. To address this gap, we introduce MINT, a new Multilingual intimacy analysis dataset covering 13,372 tweets in 10 languages including English, French, Spanish, Italian, Portuguese, Korean, Dutch, Chinese, Hindi, and Arabic along with SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis. Our task attracted 45 participants from around the world. While the participants are able to achieve overall good performance on languages in the training set, zero-shot prediction of intimacy in unseen languages remains challenging. Here we provide an overview of the task, summaries of the common approaches, and potential future directions on modeling intimacy across languages. All the relevant resources are available at https: //sites.google.com/umich.edu/ semeval-2023-tweet-intimacy.", }

Intimacy is an important social aspect of language. Computational modeling of intimacy in language could help many downstream applications like dialogue systems and offensiveness detection. Despite its importance, resources and approaches on modeling textual intimacy remain rare. To address this gap, we introduce MINT, a new Multilingual intimacy analysis dataset covering 13,372 tweets in 10 languages including English, French, Spanish, Italian, Portuguese, Korean, Dutch, Chinese, Hindi, and Arabic along with SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis. Our task attracted 45 participants from around the world. While the participants are able to achieve overall good performance on languages in the training set, zero-shot prediction of intimacy in unseen languages remains challenging. Here we provide an overview of the task, summaries of the common approaches, and potential future directions on modeling intimacy across languages. All the relevant resources are available at https: //sites.google.com/umich.edu/ semeval-2023-tweet-intimacy.

SemEval-2023 Task 9: Multilingual Tweet Intimacy Analysis

semeval-1.309

Poster

https://aclanthology.org/2023.semeval-1.310.bib

https://aclanthology.org/2023.semeval-1.310/

@inproceedings{fetahu-etal-2023-semeval, title = "{S}em{E}val-2023 Task 2: Fine-grained Multilingual Named Entity Recognition ({M}ulti{C}o{NER} 2)", author = "Fetahu, Besnik and Kar, Sudipta and Chen, Zhiyu and Rokhlenko, Oleg and Malmasi, Shervin", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.310", doi = "10.18653/v1/2023.semeval-1.310", pages = "2247--2265", abstract = "We present the findings of SemEval-2023 Task 2 on Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2). Divided into 13 tracks, the task focused on methods to identify complex fine-grained named entities (like WRITTENWORK, VEHICLE, MUSICALGRP) across 12 languages, in both monolingual and multilingual scenarios, as well as noisy settings. The task used the MultiCoNER V2 dataset, composed of 2.2 million instances in Bangla, Chinese, English, Farsi, French, German, Hindi, Italian., Portuguese, Spanish, Swedish, and Ukrainian. MultiCoNER 2 was one of the most popular tasks of SemEval-2023. It attracted 842 submissions from 47 teams, and 34 teams submitted system papers. Results showed that complex entity types such as media titles and product names were the most challenging. Methods fusing external knowledge into transformer models achieved the best performance, and the largest gains were on the Creative Work and Group classes, which are still challenging even with external knowledge. Some fine-grained classes proved to be more challenging than others, such as SCIENTIST, ARTWORK, and PRIVATECORP. We also observed that noisy data has a significant impact on model performance, with an average drop of 10{\%} on the noisy subset. The task highlights the need for future research on improving NER robustness on noisy data containing complex entities.", }

We present the findings of SemEval-2023 Task 2 on Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2). Divided into 13 tracks, the task focused on methods to identify complex fine-grained named entities (like WRITTENWORK, VEHICLE, MUSICALGRP) across 12 languages, in both monolingual and multilingual scenarios, as well as noisy settings. The task used the MultiCoNER V2 dataset, composed of 2.2 million instances in Bangla, Chinese, English, Farsi, French, German, Hindi, Italian., Portuguese, Spanish, Swedish, and Ukrainian. MultiCoNER 2 was one of the most popular tasks of SemEval-2023. It attracted 842 submissions from 47 teams, and 34 teams submitted system papers. Results showed that complex entity types such as media titles and product names were the most challenging. Methods fusing external knowledge into transformer models achieved the best performance, and the largest gains were on the Creative Work and Group classes, which are still challenging even with external knowledge. Some fine-grained classes proved to be more challenging than others, such as SCIENTIST, ARTWORK, and PRIVATECORP. We also observed that noisy data has a significant impact on model performance, with an average drop of 10{\%} on the noisy subset. The task highlights the need for future research on improving NER robustness on noisy data containing complex entities.

SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)

semeval-1.310

Poster

2305.06586

https://aclanthology.org/2023.semeval-1.311.bib

https://aclanthology.org/2023.semeval-1.311/

@inproceedings{khetan-etal-2023-semeval, title = "{S}em{E}val-2023 Task 8: Causal Medical Claim Identification and Related {PIO} Frame Extraction from Social Media Posts", author = "Khetan, Vivek and Wadhwa, Somin and Wallace, Byron and Amir, Silvio", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.311", doi = "10.18653/v1/2023.semeval-1.311", pages = "2266--2274", abstract = "Identification of medical claims from user-generated text data is an onerous but essential step for various tasks including content moderation, and hypothesis generation. SemEval-2023 Task 8 is an effort towards building those capabilities and motivating further research in this direction. This paper summarizes the details and results of shared task 8 at SemEval-2023 which involved identifying causal medical claims and extracting related Populations, Interventions, and Outcomes ({``}PIO{''}) frames from social media (Reddit) text. This shared task comprised two subtasks: (1) Causal claim identification; and (2) PIO frame extraction. In total, seven teams participated in the task. Of the seven, six provided system descriptions which we summarize here. For the first subtask, the best approach yielded a macro-averaged F-1 score of 78.40, and for the second subtask, the best approach achieved token-level F-1 scores of 40.55 for Populations, 49.71 for Interventions, and 30.08 for Outcome frames.", }

Identification of medical claims from user-generated text data is an onerous but essential step for various tasks including content moderation, and hypothesis generation. SemEval-2023 Task 8 is an effort towards building those capabilities and motivating further research in this direction. This paper summarizes the details and results of shared task 8 at SemEval-2023 which involved identifying causal medical claims and extracting related Populations, Interventions, and Outcomes ({``}PIO{''}) frames from social media (Reddit) text. This shared task comprised two subtasks: (1) Causal claim identification; and (2) PIO frame extraction. In total, seven teams participated in the task. Of the seven, six provided system descriptions which we summarize here. For the first subtask, the best approach yielded a macro-averaged F-1 score of 78.40, and for the second subtask, the best approach achieved token-level F-1 scores of 40.55 for Populations, 49.71 for Interventions, and 30.08 for Outcome frames.

SemEval-2023 Task 8: Causal Medical Claim Identification and Related PIO Frame Extraction from Social Media Posts

semeval-1.311

Poster

https://aclanthology.org/2023.semeval-1.312.bib

https://aclanthology.org/2023.semeval-1.312/

@inproceedings{frobe-etal-2023-semeval, title = "{S}em{E}val-2023 Task 5: Clickbait Spoiling", author = {Fr{\"o}be, Maik and Stein, Benno and Gollub, Tim and Hagen, Matthias and Potthast, Martin}, editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.312", doi = "10.18653/v1/2023.semeval-1.312", pages = "2275--2286", abstract = "In this overview paper, we report on the second PAN{\textasciitilde}Clickbait Challenge hosted as Task{\textasciitilde}5 at SemEval{\textasciitilde}2023. The challenge{'}s focus is to better support social media users by automatically generating short spoilers that close the curiosity gap induced by a clickbait post. We organized two subtasks: (1) spoiler type classification to assess what kind of spoiler a clickbait post warrants (e.g., a phrase), and (2) spoiler generation to generate an actual spoiler for a clickbait post.", }

In this overview paper, we report on the second PAN{\textasciitilde}Clickbait Challenge hosted as Task{\textasciitilde}5 at SemEval{\textasciitilde}2023. The challenge{'}s focus is to better support social media users by automatically generating short spoilers that close the curiosity gap induced by a clickbait post. We organized two subtasks: (1) spoiler type classification to assess what kind of spoiler a clickbait post warrants (e.g., a phrase), and (2) spoiler generation to generate an actual spoiler for a clickbait post.

SemEval-2023 Task 5: Clickbait Spoiling

semeval-1.312

Poster

https://aclanthology.org/2023.semeval-1.313.bib

https://aclanthology.org/2023.semeval-1.313/

@inproceedings{kiesel-etal-2023-semeval, title = "{S}em{E}val-2023 Task 4: {V}alue{E}val: Identification of Human Values Behind Arguments", author = "Kiesel, Johannes and Alshomary, Milad and Mirzakhmedova, Nailia and Heinrich, Maximilian and Handke, Nicolas and Wachsmuth, Henning and Stein, Benno", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.313", doi = "10.18653/v1/2023.semeval-1.313", pages = "2287--2303", abstract = "Argumentation is ubiquitous in natural language communication, from politics and media to everyday work and private life. Many arguments derive their persuasive power from human values, such as self-directed thought or tolerance, albeit often implicitly. These values are key to understanding the semantics of arguments, as they are generally accepted as justifications for why a particular option is ethically desirable. Can automated systems uncover the values on which an argument draws? To answer this question, 39 teams submitted runs to ValueEval{'}23. Using a multi-sourced dataset of over 9K arguments, the systems achieved F1-scores up to 0.87 (nature) and over 0.70 for three more of 20 universal value categories. However, many challenges remain, as evidenced by the low peak F1-score of 0.39 for stimulation, hedonism, face, and humility.", }

Argumentation is ubiquitous in natural language communication, from politics and media to everyday work and private life. Many arguments derive their persuasive power from human values, such as self-directed thought or tolerance, albeit often implicitly. These values are key to understanding the semantics of arguments, as they are generally accepted as justifications for why a particular option is ethically desirable. Can automated systems uncover the values on which an argument draws? To answer this question, 39 teams submitted runs to ValueEval{'}23. Using a multi-sourced dataset of over 9K arguments, the systems achieved F1-scores up to 0.87 (nature) and over 0.70 for three more of 20 universal value categories. However, many challenges remain, as evidenced by the low peak F1-score of 0.39 for stimulation, hedonism, face, and humility.

SemEval-2023 Task 4: ValueEval: Identification of Human Values Behind Arguments

semeval-1.313

Poster

https://aclanthology.org/2023.semeval-1.314.bib

https://aclanthology.org/2023.semeval-1.314/

@inproceedings{leonardelli-etal-2023-semeval, title = "{S}em{E}val-2023 Task 11: Learning with Disagreements ({L}e{W}i{D}i)", author = "Leonardelli, Elisa and Abercrombie, Gavin and Almanea, Dina and Basile, Valerio and Fornaciari, Tommaso and Plank, Barbara and Rieser, Verena and Uma, Alexandra and Poesio, Massimo", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.314", doi = "10.18653/v1/2023.semeval-1.314", pages = "2304--2318", abstract = "NLP datasets annotated with human judgments are rife with disagreements between the judges. This is especially true for tasks depending on subjective judgments such as sentiment analysis or offensive language detection. Particularly in these latter cases, the NLP community has come to realize that the common approach of reconciling{'} these different subjective interpretations risks misrepresenting the evidence. Many NLP researchers have therefore concluded that rather than eliminating disagreements from annotated corpora, we should preserve themindeed, some argue that corpora should aim to preserve all interpretations produced by annotators. But this approach to corpus creation for NLP has not yet been widely accepted. The objective of the Le-Wi-Di series of shared tasks is to promote this approach to developing NLP models by providing a unified framework for training and evaluating with such datasets. We report on the second such shared task, which differs from the first edition in three crucial respects: (i) it focuses entirely on NLP, instead of both NLP and computer vision tasks in its first edition; (ii) it focuses on subjective tasks, instead of covering different types of disagreements as training with aggregated labels for subjective NLP tasks is in effect a misrepresentation of the data; and (iii) for the evaluation, we concentrated on soft approaches to evaluation. This second edition of Le-Wi-Di attracted a wide array of partici- pants resulting in 13 shared task submission papers.", }

NLP datasets annotated with human judgments are rife with disagreements between the judges. This is especially true for tasks depending on subjective judgments such as sentiment analysis or offensive language detection. Particularly in these latter cases, the NLP community has come to realize that the common approach of reconciling{'} these different subjective interpretations risks misrepresenting the evidence. Many NLP researchers have therefore concluded that rather than eliminating disagreements from annotated corpora, we should preserve themindeed, some argue that corpora should aim to preserve all interpretations produced by annotators. But this approach to corpus creation for NLP has not yet been widely accepted. The objective of the Le-Wi-Di series of shared tasks is to promote this approach to developing NLP models by providing a unified framework for training and evaluating with such datasets. We report on the second such shared task, which differs from the first edition in three crucial respects: (i) it focuses entirely on NLP, instead of both NLP and computer vision tasks in its first edition; (ii) it focuses on subjective tasks, instead of covering different types of disagreements as training with aggregated labels for subjective NLP tasks is in effect a misrepresentation of the data; and (iii) for the evaluation, we concentrated on soft approaches to evaluation. This second edition of Le-Wi-Di attracted a wide array of partici- pants resulting in 13 shared task submission papers.

SemEval-2023 Task 11: Learning with Disagreements (LeWiDi)

semeval-1.314

Poster

2304.14803

https://aclanthology.org/2023.semeval-1.315.bib

https://aclanthology.org/2023.semeval-1.315/

@inproceedings{muhammad-etal-2023-semeval, title = "{S}em{E}val-2023 Task 12: Sentiment Analysis for {A}frican Languages ({A}fri{S}enti-{S}em{E}val)", author = "Muhammad, Shamsuddeen Hassan and Abdulmumin, Idris and Yimam, Seid Muhie and Adelani, David Ifeoluwa and Ahmad, Ibrahim Said and Ousidhoum, Nedjma and Ayele, Abinew Ali and Mohammad, Saif and Beloucif, Meriem and Ruder, Sebastian", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.315", doi = "10.18653/v1/2023.semeval-1.315", pages = "2319--2337", abstract = "We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at \url{https://github.com/afrisenti-semeval/afrisent-semeval-2023}. AfriSenti-SemEval is a sentiment classification challenge in 14 African languages: Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yorb (Muhammad et al., 2023), using data labeled with 3 sentiment classes. We present three subtasks: (1) Task A: monolingual classification, which received 44 submissions; (2) Task B: multilingual classification, which received 32 submissions; and (3) Task C: zero-shot classification, which received 34 submissions. The best performance for tasks A and B was achieved by NLNDE team with 71.31 and 75.06 weighted F1, respectively. UCAS-IIE-NLP achieved the best average score for task C with 58.15 weighted F1. We describe the various approaches adopted by the top 10 systems and their approaches.", }

We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at \url{https://github.com/afrisenti-semeval/afrisent-semeval-2023}. AfriSenti-SemEval is a sentiment classification challenge in 14 African languages: Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yorb (Muhammad et al., 2023), using data labeled with 3 sentiment classes. We present three subtasks: (1) Task A: monolingual classification, which received 44 submissions; (2) Task B: multilingual classification, which received 32 submissions; and (3) Task C: zero-shot classification, which received 34 submissions. The best performance for tasks A and B was achieved by NLNDE team with 71.31 and 75.06 weighted F1, respectively. UCAS-IIE-NLP achieved the best average score for task C with 58.15 weighted F1. We describe the various approaches adopted by the top 10 systems and their approaches.

SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)

semeval-1.315

Poster

2304.06845

https://aclanthology.org/2023.semeval-1.316.bib

https://aclanthology.org/2023.semeval-1.316/

@inproceedings{mahendra-etal-2023-ittc, title = "{ITTC} at {S}em{E}val 2023-Task 7: Document Retrieval and Sentence Similarity for Evidence Retrieval in Clinical Trial Data", author = "Mahendra, Rahmad and Spina, Damiano and Verspoor, Karin", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.316", doi = "10.18653/v1/2023.semeval-1.316", pages = "2338--2342", abstract = "This paper describes the submissions of the Natural Language Processing (NLP) team from the Australian Research Council Industrial Transformation Training Centre (ITTC) for Cognitive Computing in Medical Technologies to the SemEval 2023 Task 7, i.e., multi-evidence natural language inference for clinical trial data (NLI4CT). More specifically, we were working on subtask 2 whose objective is to identify the relevant parts of the premise from clinical trial report that justify the truth of information in the statement. We approach the evidence retrieval problem as a document retrieval and sentence similarity task. Our results show that the task poses some challenges which involve dealing with complex sentences and implicit evidences.", }

This paper describes the submissions of the Natural Language Processing (NLP) team from the Australian Research Council Industrial Transformation Training Centre (ITTC) for Cognitive Computing in Medical Technologies to the SemEval 2023 Task 7, i.e., multi-evidence natural language inference for clinical trial data (NLI4CT). More specifically, we were working on subtask 2 whose objective is to identify the relevant parts of the premise from clinical trial report that justify the truth of information in the statement. We approach the evidence retrieval problem as a document retrieval and sentence similarity task. Our results show that the task poses some challenges which involve dealing with complex sentences and implicit evidences.

ITTC at SemEval 2023-Task 7: Document Retrieval and Sentence Similarity for Evidence Retrieval in Clinical Trial Data

semeval-1.316

Poster

https://aclanthology.org/2023.semeval-1.317.bib

https://aclanthology.org/2023.semeval-1.317/

@inproceedings{piskorski-etal-2023-semeval, title = "{S}em{E}val-2023 Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", author = "Piskorski, Jakub and Stefanovitch, Nicolas and Da San Martino, Giovanni and Nakov, Preslav", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.317", doi = "10.18653/v1/2023.semeval-1.317", pages = "2343--2361", abstract = "We describe SemEval-2023 task 3 on Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multilingual Setup: the dataset, the task organization process, the evaluation setup, the results, and the participating systems. The task focused on news articles in nine languages (six known to the participants upfront: English, French, German, Italian, Polish, and Russian), and three additional ones revealed to the participants at the testing phase: Spanish, Greek, and Georgian). The task featured three subtasks: (1) determining the genre of the article (opinion, reporting, or satire), (2) identifying one or more frames used in an article from a pool of 14 generic frames, and (3) identify the persuasion techniques used in each paragraph of the article, using a taxonomy of 23 persuasion techniques. This was a very popular task: a total of 181 teams registered to participate, and 41 eventually made an official submission on the test set.", }

We describe SemEval-2023 task 3 on Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multilingual Setup: the dataset, the task organization process, the evaluation setup, the results, and the participating systems. The task focused on news articles in nine languages (six known to the participants upfront: English, French, German, Italian, Polish, and Russian), and three additional ones revealed to the participants at the testing phase: Spanish, Greek, and Georgian). The task featured three subtasks: (1) determining the genre of the article (opinion, reporting, or satire), (2) identifying one or more frames used in an article from a pool of 14 generic frames, and (3) identify the persuasion techniques used in each paragraph of the article, using a taxonomy of 23 persuasion techniques. This was a very popular task: a total of 181 teams registered to participate, and 41 eventually made an official submission on the test set.

SemEval-2023 Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup

semeval-1.317

Poster

https://aclanthology.org/2023.semeval-1.318.bib

https://aclanthology.org/2023.semeval-1.318/

@inproceedings{modi-etal-2023-semeval, title = "{S}em{E}val-2023 Task 6: {L}egal{E}val - Understanding Legal Texts", author = "Modi, Ashutosh and Kalamkar, Prathamesh and Karn, Saurabh and Tiwari, Aman and Joshi, Abhinav and Tanikella, Sai Kiran and Guha, Shouvik Kumar and Malhan, Sachin and Raghavan, Vivek", editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa}, booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.318", doi = "10.18653/v1/2023.semeval-1.318", pages = "2362--2374", abstract = "In populous countries, pending legal cases have been growing exponentially. There is a need for developing NLP-based techniques for processing and automatically understanding legal documents. To promote research in the area of Legal NLP we organized the shared task LegalEval - Understanding Legal Texts at SemEval 2023. LegalEval task has three sub-tasks: Task-A (Rhetorical Roles Labeling) is about automatically structuring legal documents into semantically coherent units, Task-B (Legal Named Entity Recognition) deals with identifying relevant entities in a legal document and Task-C (Court Judgement Prediction with Explanation) explores the possibility of automatically predicting the outcome of a legal case along with providing an explanation for the prediction. In total 26 teams (approx. 100 participants spread across the world) submitted systems paper. In each of the sub-tasks, the proposed systems outperformed the baselines; however, there is a lot of scope for improvement. This paper describes the tasks, and analyzes techniques proposed by various teams.", }

In populous countries, pending legal cases have been growing exponentially. There is a need for developing NLP-based techniques for processing and automatically understanding legal documents. To promote research in the area of Legal NLP we organized the shared task LegalEval - Understanding Legal Texts at SemEval 2023. LegalEval task has three sub-tasks: Task-A (Rhetorical Roles Labeling) is about automatically structuring legal documents into semantically coherent units, Task-B (Legal Named Entity Recognition) deals with identifying relevant entities in a legal document and Task-C (Court Judgement Prediction with Explanation) explores the possibility of automatically predicting the outcome of a legal case along with providing an explanation for the prediction. In total 26 teams (approx. 100 participants spread across the world) submitted systems paper. In each of the sub-tasks, the proposed systems outperformed the baselines; however, there is a lot of scope for improvement. This paper describes the tasks, and analyzes techniques proposed by various teams.

SemEval-2023 Task 6: LegalEval - Understanding Legal Texts

semeval-1.318

Poster

https://aclanthology.org/2023.sicon-1.1.bib

https://aclanthology.org/2023.sicon-1.1/

@inproceedings{gong-etal-2023-eliciting, title = "Eliciting Rich Positive Emotions in Dialogue Generation", author = "Gong, Ziwei and Min, Qingkai and Zhang, Yue", editor = "Chawla, Kushal and Shi, Weiyan", booktitle = "Proceedings of the First Workshop on Social Influence in Conversations (SICon 2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sicon-1.1", doi = "10.18653/v1/2023.sicon-1.1", pages = "1--8", abstract = "Positive emotion elicitation aims at evoking positive emotion states in human users in open-domain dialogue generation. However, most work focuses on inducing a single-dimension of positive sentiment using human annotated datasets, which limits the scale of the training dataset. In this paper, we propose to model various emotions in large unannotated conversations, such as joy, trust and anticipation, by leveraging a latent variable to control the emotional intention of the response. Our proposed emotion-eliciting-Conditional-Variational-AutoEncoder (EE-CVAE) model generates more diverse and emotionally-intelligent responses compared to single-dimension baseline models in human evaluation.", }

Positive emotion elicitation aims at evoking positive emotion states in human users in open-domain dialogue generation. However, most work focuses on inducing a single-dimension of positive sentiment using human annotated datasets, which limits the scale of the training dataset. In this paper, we propose to model various emotions in large unannotated conversations, such as joy, trust and anticipation, by leveraging a latent variable to control the emotional intention of the response. Our proposed emotion-eliciting-Conditional-Variational-AutoEncoder (EE-CVAE) model generates more diverse and emotionally-intelligent responses compared to single-dimension baseline models in human evaluation.

Eliciting Rich Positive Emotions in Dialogue Generation

sicon-1.1

Poster

https://aclanthology.org/2023.sicon-1.2.bib

https://aclanthology.org/2023.sicon-1.2/

@inproceedings{bose-etal-2023-detoxifying, title = "Detoxifying Online Discourse: A Guided Response Generation Approach for Reducing Toxicity in User-Generated Text", author = "Bose, Ritwik and Perera, Ian and Dorr, Bonnie", editor = "Chawla, Kushal and Shi, Weiyan", booktitle = "Proceedings of the First Workshop on Social Influence in Conversations (SICon 2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sicon-1.2", doi = "10.18653/v1/2023.sicon-1.2", pages = "9--14", abstract = "The expression of opinions, stances, and moral foundations on social media often coincide with toxic, divisive, or inflammatory language that can make constructive discourse across communities difficult. Natural language generation methods could provide a means to reframe or reword such expressions in a way that fosters more civil discourse, yet current Large Language Model (LLM) methods tend towards language that is too generic or formal to seem authentic for social media discussions. We present preliminary work on training LLMs to maintain authenticity while presenting a community{'}s ideas and values in a constructive, non-toxic manner.", }

The expression of opinions, stances, and moral foundations on social media often coincide with toxic, divisive, or inflammatory language that can make constructive discourse across communities difficult. Natural language generation methods could provide a means to reframe or reword such expressions in a way that fosters more civil discourse, yet current Large Language Model (LLM) methods tend towards language that is too generic or formal to seem authentic for social media discussions. We present preliminary work on training LLMs to maintain authenticity while presenting a community{'}s ideas and values in a constructive, non-toxic manner.

Detoxifying Online Discourse: A Guided Response Generation Approach for Reducing Toxicity in User-Generated Text

sicon-1.2

Poster

https://aclanthology.org/2023.sicon-1.3.bib

https://aclanthology.org/2023.sicon-1.3/

@inproceedings{griffin-etal-2023-large, title = "Large Language Models respond to Influence like Humans", author = "Griffin, Lewis and Kleinberg, Bennett and Mozes, Maximilian and Mai, Kimberly and Vau, Maria Do Mar and Caldwell, Matthew and Mavor-Parker, Augustine", editor = "Chawla, Kushal and Shi, Weiyan", booktitle = "Proceedings of the First Workshop on Social Influence in Conversations (SICon 2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sicon-1.3", doi = "10.18653/v1/2023.sicon-1.3", pages = "15--24", abstract = "Two studies tested the hypothesis that a Large Language Model (LLM) can be used to model psychological change following exposure to influential input. The first study tested a generic mode of influence - the Illusory Truth Effect (ITE) - where earlier exposure to a statement boosts a later truthfulness test rating. Analysis of newly collected data from human and LLM-simulated subjects (1000 of each) showed the same pattern of effects in both populations; although with greater per statement variability for the LLM. The second study concerns a specific mode of influence {--} populist framing of news to increase its persuasion and political mobilization. Newly collected data from simulated subjects was compared to previously published data from a 15 country experiment on 7286 human participants. Several effects from the human study were replicated by the simulated study, including ones that surprised the authors of the human study by contradicting their theoretical expectations; but some significant relationships found in human data were not present in the LLM data. Together the two studies support the view that LLMs have potential to act as models of the effect of influence.", }

Two studies tested the hypothesis that a Large Language Model (LLM) can be used to model psychological change following exposure to influential input. The first study tested a generic mode of influence - the Illusory Truth Effect (ITE) - where earlier exposure to a statement boosts a later truthfulness test rating. Analysis of newly collected data from human and LLM-simulated subjects (1000 of each) showed the same pattern of effects in both populations; although with greater per statement variability for the LLM. The second study concerns a specific mode of influence {--} populist framing of news to increase its persuasion and political mobilization. Newly collected data from simulated subjects was compared to previously published data from a 15 country experiment on 7286 human participants. Several effects from the human study were replicated by the simulated study, including ones that surprised the authors of the human study by contradicting their theoretical expectations; but some significant relationships found in human data were not present in the LLM data. Together the two studies support the view that LLMs have potential to act as models of the effect of influence.

Large Language Models respond to Influence like Humans

sicon-1.3

Poster

https://aclanthology.org/2023.sicon-1.4.bib

https://aclanthology.org/2023.sicon-1.4/

@inproceedings{fraser-etal-2023-makes, title = "What Makes a Good Counter-Stereotype? Evaluating Strategies for Automated Responses to Stereotypical Text", author = "Fraser, Kathleen and Kiritchenko, Svetlana and Nejadgholi, Isar and Kerkhof, Anna", editor = "Chawla, Kushal and Shi, Weiyan", booktitle = "Proceedings of the First Workshop on Social Influence in Conversations (SICon 2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sicon-1.4", doi = "10.18653/v1/2023.sicon-1.4", pages = "25--38", abstract = "When harmful social stereotypes are expressed on a public platform, they must be addressed in a way that educates and informs both the original poster and other readers, without causing offence or perpetuating new stereotypes. In this paper, we synthesize findings from psychology and computer science to propose a set of potential counter-stereotype strategies. We then automatically generate such counter-stereotypes using ChatGPT, and analyze their correctness and expected effectiveness at reducing stereotypical associations. We identify the strategies of denouncing stereotypes, warning of consequences, and using an empathetic tone as three promising strategies to be further tested.", }

When harmful social stereotypes are expressed on a public platform, they must be addressed in a way that educates and informs both the original poster and other readers, without causing offence or perpetuating new stereotypes. In this paper, we synthesize findings from psychology and computer science to propose a set of potential counter-stereotype strategies. We then automatically generate such counter-stereotypes using ChatGPT, and analyze their correctness and expected effectiveness at reducing stereotypical associations. We identify the strategies of denouncing stereotypes, warning of consequences, and using an empathetic tone as three promising strategies to be further tested.

What Makes a Good Counter-Stereotype? Evaluating Strategies for Automated Responses to Stereotypical Text

sicon-1.4

Poster

https://aclanthology.org/2023.sicon-1.5.bib

https://aclanthology.org/2023.sicon-1.5/

@inproceedings{anastasiou-de-libbo-2023-bcause, title = "{BC}ause: Reducing group bias and promoting cohesive discussion in online deliberation processes through a simple and engaging online deliberation tool", author = "Anastasiou, Lucas and De Liddo, Anna", editor = "Chawla, Kushal and Shi, Weiyan", booktitle = "Proceedings of the First Workshop on Social Influence in Conversations (SICon 2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sicon-1.5", doi = "10.18653/v1/2023.sicon-1.5", pages = "39--49", abstract = "Facilitating healthy online deliberation in terms of sensemaking and collaboration of discussion participants proves extremely challenging due to a number of known negative effects of online communication on social media platforms. We start from concerns and aspirations about the use of existing online discussion systems as distilled in previous literature, we then combine them with lessons learned on design and engineering practices from our research team, to inform the design of an easy-to-use tool (BCause.app) that enables higher quality discussions than traditional social media. We describe the design of this tool, highlighting the main interaction features that distinguish it from common social media, namely: i. the low-cost argumentation structuring of the conversations with direct replies; ii. and the distinctive use of reflective feedback rather than appreciative-only feedback. We then present the results of a controlled A/B experiment in which we show that the presence of argumentative and cognitive reflective discussion elements produces better social interaction with less polarization and promotes a more cohesive discussion than common social media-like interactions.", }

Facilitating healthy online deliberation in terms of sensemaking and collaboration of discussion participants proves extremely challenging due to a number of known negative effects of online communication on social media platforms. We start from concerns and aspirations about the use of existing online discussion systems as distilled in previous literature, we then combine them with lessons learned on design and engineering practices from our research team, to inform the design of an easy-to-use tool (BCause.app) that enables higher quality discussions than traditional social media. We describe the design of this tool, highlighting the main interaction features that distinguish it from common social media, namely: i. the low-cost argumentation structuring of the conversations with direct replies; ii. and the distinctive use of reflective feedback rather than appreciative-only feedback. We then present the results of a controlled A/B experiment in which we show that the presence of argumentative and cognitive reflective discussion elements produces better social interaction with less polarization and promotes a more cohesive discussion than common social media-like interactions.

BCause: Reducing group bias and promoting cohesive discussion in online deliberation processes through a simple and engaging online deliberation tool

sicon-1.5

Poster

https://aclanthology.org/2023.sicon-1.6.bib

https://aclanthology.org/2023.sicon-1.6/

@inproceedings{gari-soler-etal-2023-measuring, title = "Measuring Lexico-Semantic Alignment in Debates with Contextualized Word Representations", author = "Gar{\'\i} Soler, Aina and Labeau, Matthieu and Clavel, Chlo{\'e}", editor = "Chawla, Kushal and Shi, Weiyan", booktitle = "Proceedings of the First Workshop on Social Influence in Conversations (SICon 2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sicon-1.6", doi = "10.18653/v1/2023.sicon-1.6", pages = "50--63", abstract = "Dialog participants sometimes align their linguistic styles, e.g., they use the same words and syntactic constructions as their interlocutors. We propose to investigate the notion of lexico-semantic alignment: to what extent do speakers convey the same meaning when they use the same words? We design measures of lexico-semantic alignment relying on contextualized word representations. We show that they reflect interesting semantic differences between the two sides of a debate and that they can assist in the task of debate{'}s winner prediction.", }

Dialog participants sometimes align their linguistic styles, e.g., they use the same words and syntactic constructions as their interlocutors. We propose to investigate the notion of lexico-semantic alignment: to what extent do speakers convey the same meaning when they use the same words? We design measures of lexico-semantic alignment relying on contextualized word representations. We show that they reflect interesting semantic differences between the two sides of a debate and that they can assist in the task of debate{'}s winner prediction.

Measuring Lexico-Semantic Alignment in Debates with Contextualized Word Representations

sicon-1.6

Poster

https://aclanthology.org/2023.sicon-1.7.bib

https://aclanthology.org/2023.sicon-1.7/

@inproceedings{ananthasubramaniam-etal-2023-exploring, title = "Exploring Linguistic Style Matching in Online Communities: The Role of Social Context and Conversation Dynamics", author = "Ananthasubramaniam, Aparna and Chen, Hong and Yan, Jason and Alkiek, Kenan and Pei, Jiaxin and Seth, Agrima and Dunagan, Lavinia and Choi, Minje and Litterer, Benjamin and Jurgens, David", editor = "Chawla, Kushal and Shi, Weiyan", booktitle = "Proceedings of the First Workshop on Social Influence in Conversations (SICon 2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sicon-1.7", doi = "10.18653/v1/2023.sicon-1.7", pages = "64--74", abstract = "Linguistic style matching (LSM) in conversations can be reflective of several aspects of social influence such as power or persuasion. However, how LSM relates to the outcomes of online communication on platforms such as Reddit is an unknown question. In this study, we analyze a large corpus of two-party conversation threads in Reddit where we identify all occurrences of LSM using two types of style: the use of function words and formality. Using this framework, we examine how levels of LSM differ in conversations depending on several social factors within Reddit: post and subreddit features, conversation depth, user tenure, and the controversiality of a comment. Finally, we measure the change of LSM following loss of status after community banning. Our findings reveal the interplay of LSM in Reddit conversations with several community metrics, suggesting the importance of understanding conversation engagement when understanding community dynamics.", }

Linguistic style matching (LSM) in conversations can be reflective of several aspects of social influence such as power or persuasion. However, how LSM relates to the outcomes of online communication on platforms such as Reddit is an unknown question. In this study, we analyze a large corpus of two-party conversation threads in Reddit where we identify all occurrences of LSM using two types of style: the use of function words and formality. Using this framework, we examine how levels of LSM differ in conversations depending on several social factors within Reddit: post and subreddit features, conversation depth, user tenure, and the controversiality of a comment. Finally, we measure the change of LSM following loss of status after community banning. Our findings reveal the interplay of LSM in Reddit conversations with several community metrics, suggesting the importance of understanding conversation engagement when understanding community dynamics.

Exploring Linguistic Style Matching in Online Communities: The Role of Social Context and Conversation Dynamics

sicon-1.7

Poster

2307.02758

https://aclanthology.org/2023.sigmorphon-1.2.bib

https://aclanthology.org/2023.sigmorphon-1.2/

@inproceedings{elsner-needle-2023-translating, title = "Translating a low-resource language using {GPT}-3 and a human-readable dictionary", author = "Elsner, Micha and Needle, Jordan", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.2", doi = "10.18653/v1/2023.sigmorphon-1.2", pages = "1--13", abstract = "We investigate how well words in the polysynthetic language Inuktitut can be translated by combining dictionary definitions, without use of a neural machine translation model trained on parallel text. Such a translation system would allow natural language technology to benefit from resources designed for community use in a language revitalization or education program, rather than requiring a separate parallel corpus. We show that the text-to-text generation capabilities of GPT-3 allow it to perform this task with BLEU scores of up to 18.5. We investigate prompting GPT-3 to provide multiple translations, which can help slightly, and providing it with grammar information, which is mostly ineffective. Finally, we test GPT-3{'}s ability to derive morpheme definitions from whole-word translations, but find this process is prone to errors including hallucinations.", }

We investigate how well words in the polysynthetic language Inuktitut can be translated by combining dictionary definitions, without use of a neural machine translation model trained on parallel text. Such a translation system would allow natural language technology to benefit from resources designed for community use in a language revitalization or education program, rather than requiring a separate parallel corpus. We show that the text-to-text generation capabilities of GPT-3 allow it to perform this task with BLEU scores of up to 18.5. We investigate prompting GPT-3 to provide multiple translations, which can help slightly, and providing it with grammar information, which is mostly ineffective. Finally, we test GPT-3{'}s ability to derive morpheme definitions from whole-word translations, but find this process is prone to errors including hallucinations.

Translating a low-resource language using GPT-3 and a human-readable dictionary

sigmorphon-1.2

Poster

https://aclanthology.org/2023.sigmorphon-1.3.bib

https://aclanthology.org/2023.sigmorphon-1.3/

@inproceedings{pawar-etal-2023-evaluating, title = "Evaluating Cross Lingual Transfer for Morphological Analysis: a Case Study of {I}ndian Languages", author = "Pawar, Siddhesh and Bhattacharyya, Pushpak and Talukdar, Partha", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.3", doi = "10.18653/v1/2023.sigmorphon-1.3", pages = "14--26", abstract = "Recent advances in pretrained multilingual models such as Multilingual T5 (mT5) have facilitated cross-lingual transfer by learning shared representations across languages. Leveraging pretrained multilingual models for scaling morphology analyzers to low-resource languages is a unique opportunity that has been under-explored so far. We investigate this line of research in the context of Indian languages, focusing on two important morphological sub-tasks: root word extraction and tagging morphosyntactic descriptions (MSD), viz., gender, number, and person (GNP). We experiment with six Indian languages from two language families (Dravidian and Indo-Aryan) to train a multilingual morphology analyzers for the first time for Indian languages. We demonstrate the usability of multilingual models for few-shot cross-lingual transfer through an average 7{\%} increase in GNP tagging in a cross-lingual setting as compared to a monolingual setting through controlled experiments. We provide an overview of the state of the datasets available related to our tasks and point-out a few modeling limitations due to datasets. Lastly, we analyze the cross-lingual transfer of morphological tags for verbs and nouns, which provides a proxy for the quality of representations of word markings learned by the model.", }

Recent advances in pretrained multilingual models such as Multilingual T5 (mT5) have facilitated cross-lingual transfer by learning shared representations across languages. Leveraging pretrained multilingual models for scaling morphology analyzers to low-resource languages is a unique opportunity that has been under-explored so far. We investigate this line of research in the context of Indian languages, focusing on two important morphological sub-tasks: root word extraction and tagging morphosyntactic descriptions (MSD), viz., gender, number, and person (GNP). We experiment with six Indian languages from two language families (Dravidian and Indo-Aryan) to train a multilingual morphology analyzers for the first time for Indian languages. We demonstrate the usability of multilingual models for few-shot cross-lingual transfer through an average 7{\%} increase in GNP tagging in a cross-lingual setting as compared to a monolingual setting through controlled experiments. We provide an overview of the state of the datasets available related to our tasks and point-out a few modeling limitations due to datasets. Lastly, we analyze the cross-lingual transfer of morphological tags for verbs and nouns, which provides a proxy for the quality of representations of word markings learned by the model.

Evaluating Cross Lingual Transfer for Morphological Analysis: a Case Study of Indian Languages

sigmorphon-1.3

Poster

https://aclanthology.org/2023.sigmorphon-1.4.bib

https://aclanthology.org/2023.sigmorphon-1.4/

@inproceedings{abudouwaili-etal-2023-joint, title = "Joint Learning Model for Low-Resource Agglutinative Language Morphological Tagging", author = "Abudouwaili, Gulinigeer and Abiderexiti, Kahaerjiang and Yi, Nian and Wumaier, Aishan", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.4", doi = "10.18653/v1/2023.sigmorphon-1.4", pages = "27--37", abstract = "Due to the lack of data resources, rule-based or transfer learning is mainly used in the morphological tagging of low-resource languages. However, these methods require expert knowledge, ignore contextual features, and have error propagation. Therefore, we propose a joint morphological tagger for low-resource agglutinative languages to alleviate the above challenges. First, we represent the contextual input with multi-dimensional features of agglutinative words. Second, joint training reduces the direct impact of part-of-speech errors on morphological features and increases the indirect influence between the two types of labels through a fusion mechanism. Finally, our model separately predicts part-of-speech and morphological features. Part-of-speech tagging is regarded as sequence tagging. When predicting morphological features, two-label adjacency graphs are dynamically reconstructed by integrating multilingual global features and monolingual local features. Then, a graph convolution network is used to learn the higher-order intersection of labels. A series of experiments show that the proposed model in this paper is superior to other comparative models.", }

Due to the lack of data resources, rule-based or transfer learning is mainly used in the morphological tagging of low-resource languages. However, these methods require expert knowledge, ignore contextual features, and have error propagation. Therefore, we propose a joint morphological tagger for low-resource agglutinative languages to alleviate the above challenges. First, we represent the contextual input with multi-dimensional features of agglutinative words. Second, joint training reduces the direct impact of part-of-speech errors on morphological features and increases the indirect influence between the two types of labels through a fusion mechanism. Finally, our model separately predicts part-of-speech and morphological features. Part-of-speech tagging is regarded as sequence tagging. When predicting morphological features, two-label adjacency graphs are dynamically reconstructed by integrating multilingual global features and monolingual local features. Then, a graph convolution network is used to learn the higher-order intersection of labels. A series of experiments show that the proposed model in this paper is superior to other comparative models.

Joint Learning Model for Low-Resource Agglutinative Language Morphological Tagging

sigmorphon-1.4

Poster

https://aclanthology.org/2023.sigmorphon-1.5.bib

https://aclanthology.org/2023.sigmorphon-1.5/

@inproceedings{ahmadi-mahmudi-2023-revisiting, title = "Revisiting and Amending {C}entral {K}urdish Data on {U}ni{M}orph 4.0", author = "Ahmadi, Sina and Mahmudi, Aso", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.5", doi = "10.18653/v1/2023.sigmorphon-1.5", pages = "38--48", abstract = "UniMorph{--}the Universal Morphology project is a collaborative initiative to create and maintain morphological data and organize numerous related tasks for various language processing communities. The morphological data is provided by linguists for over 160 languages in the latest version of UniMorph 4.0. This paper sheds light on the Central Kurdish data on UniMorph 4.0 by analyzing the existing data, its fallacies, and systematic morphological errors. It also presents an approach to creating more reliable morphological data by considering various specific phenomena in Central Kurdish that have not been addressed previously, such as Izafe and several enclitics.", }

UniMorph{--}the Universal Morphology project is a collaborative initiative to create and maintain morphological data and organize numerous related tasks for various language processing communities. The morphological data is provided by linguists for over 160 languages in the latest version of UniMorph 4.0. This paper sheds light on the Central Kurdish data on UniMorph 4.0 by analyzing the existing data, its fallacies, and systematic morphological errors. It also presents an approach to creating more reliable morphological data by considering various specific phenomena in Central Kurdish that have not been addressed previously, such as Izafe and several enclitics.

Revisiting and Amending Central Kurdish Data on UniMorph 4.0

sigmorphon-1.5

Poster

https://aclanthology.org/2023.sigmorphon-1.6.bib

https://aclanthology.org/2023.sigmorphon-1.6/

@inproceedings{masson-carson-berndsen-2023-investigating, title = "Investigating Phoneme Similarity with Artificially Accented Speech", author = "Masson, Margot and Carson-berndsen, Julie", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.6", doi = "10.18653/v1/2023.sigmorphon-1.6", pages = "49--57", abstract = "While the deep learning revolution has led to significant performance improvements in speech recognition, accented speech remains a challenge. Current approaches to this challenge typically do not seek to understand and provide explanations for the variations of accented speech, whether they stem from native regional variation or non-native error patterns. This paper seeks to address non-native speaker variations from both a knowledge-based and a data-driven perspective. We propose to approximate non-native accented-speech pronunciation patterns by the means of two approaches: based on phonetic and phonological knowledge on the one hand and inferred from a text-to-speech system on the other. Artificial speech is then generated with a range of variants which have been captured in confusion matrices representing phoneme similarities. We then show that non-native accent confusions actually propagate to the transcription from the ASR, thus suggesting that the inference of accent specific phoneme confusions is achievable from artificial speech.", }

While the deep learning revolution has led to significant performance improvements in speech recognition, accented speech remains a challenge. Current approaches to this challenge typically do not seek to understand and provide explanations for the variations of accented speech, whether they stem from native regional variation or non-native error patterns. This paper seeks to address non-native speaker variations from both a knowledge-based and a data-driven perspective. We propose to approximate non-native accented-speech pronunciation patterns by the means of two approaches: based on phonetic and phonological knowledge on the one hand and inferred from a text-to-speech system on the other. Artificial speech is then generated with a range of variants which have been captured in confusion matrices representing phoneme similarities. We then show that non-native accent confusions actually propagate to the transcription from the ASR, thus suggesting that the inference of accent specific phoneme confusions is achievable from artificial speech.

Investigating Phoneme Similarity with Artificially Accented Speech

sigmorphon-1.6

Poster

https://aclanthology.org/2023.sigmorphon-1.7.bib

https://aclanthology.org/2023.sigmorphon-1.7/

@inproceedings{mortensen-etal-2023-generalized, title = "Generalized Glossing Guidelines: An Explicit, Human- and Machine-Readable, Item-and-Process Convention for Morphological Annotation", author = "Mortensen, David R. and Gulsen, Ela and He, Taiqi and Robinson, Nathaniel and Amith, Jonathan and Tjuatja, Lindia and Levin, Lori", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.7", doi = "10.18653/v1/2023.sigmorphon-1.7", pages = "58--67", abstract = "Interlinear glossing provides a vital type of morphosyntactic annotation, both for linguists and language revitalists, and numerous conventions exist for representing it formally and computationally. Some of these formats are human readable; others are machine readable. Some are easy to edit with general-purpose tools. Few represent non-concatentative processes like infixation, reduplication, mutation, truncation, and tonal overwriting in a consistent and formally rigorous way (on par with affixation). We propose an annotation convention{\^a}€{''}Generalized Glossing Guidelines (GGG) that combines all of these positive properties using an Item-and-Process (IP) framework. We describe the format, demonstrate its linguistic adequacy, and compare it with two other interlinear glossed text annotation schemes.", }

Interlinear glossing provides a vital type of morphosyntactic annotation, both for linguists and language revitalists, and numerous conventions exist for representing it formally and computationally. Some of these formats are human readable; others are machine readable. Some are easy to edit with general-purpose tools. Few represent non-concatentative processes like infixation, reduplication, mutation, truncation, and tonal overwriting in a consistent and formally rigorous way (on par with affixation). We propose an annotation convention{\^a}€{''}Generalized Glossing Guidelines (GGG) that combines all of these positive properties using an Item-and-Process (IP) framework. We describe the format, demonstrate its linguistic adequacy, and compare it with two other interlinear glossed text annotation schemes.

Generalized Glossing Guidelines: An Explicit, Human- and Machine-Readable, Item-and-Process Convention for Morphological Annotation

sigmorphon-1.7

Poster

https://aclanthology.org/2023.sigmorphon-1.8.bib

https://aclanthology.org/2023.sigmorphon-1.8/

@inproceedings{arora-etal-2023-jambu, title = "Jambu: A historical linguistic database for {S}outh {A}sian languages", author = "Arora, Aryaman and Farris, Adam and Basu, Samopriya and Kolichala, Suresh", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.8", doi = "10.18653/v1/2023.sigmorphon-1.8", pages = "68--77", abstract = "We introduce JAMBU, a cognate database of South Asian languages which unifies dozens of previous sources in a structured and accessible format. The database includes nearly 287k lemmata from 602 lects, grouped together in 23k sets of cognates. We outline the data wrangling necessary to compile the dataset and train neural models for reflex prediction on the Indo- Aryan subset of the data. We hope that JAMBU is an invaluable resource for all historical linguists and Indologists, and look towards further improvement and expansion of the database.", }

We introduce JAMBU, a cognate database of South Asian languages which unifies dozens of previous sources in a structured and accessible format. The database includes nearly 287k lemmata from 602 lects, grouped together in 23k sets of cognates. We outline the data wrangling necessary to compile the dataset and train neural models for reflex prediction on the Indo- Aryan subset of the data. We hope that JAMBU is an invaluable resource for all historical linguists and Indologists, and look towards further improvement and expansion of the database.

Jambu: A historical linguistic database for South Asian languages

sigmorphon-1.8

Poster

2306.02514

https://aclanthology.org/2023.sigmorphon-1.9.bib

https://aclanthology.org/2023.sigmorphon-1.9/

@inproceedings{shandilya-palmer-2023-lightweight, title = "Lightweight morpheme labeling in context: Using structured linguistic representations to support linguistic analysis for the language documentation context", author = "Shandilya, Bhargav and Palmer, Alexis", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.9", doi = "10.18653/v1/2023.sigmorphon-1.9", pages = "78--92", abstract = "Linguistic analysis is a core task in the process of documenting, analyzing, and describing endangered and less-studied languages. In addition to providing insight into the properties of the language being studied, having tools to automatically label words in a language for grammatical category and morphological features can support a range of applications useful for language pedagogy and revitalization. At the same time, most modern NLP methods for these tasks require both large amounts of data in the language and compute costs well beyond the capacity of most research groups and language communities. In this paper, we present a gloss-to-gloss (g2g) model for linguistic analysis (specifically, morphological analysis and part-of-speech tagging) that is lightweight in terms of both data requirements and computational expense. The model is designed for the interlinear glossed text (IGT) format, in which we expect the source text of a sentence in a low-resource language, a translation of that sentence into a language of wider communication, and a detailed glossing of the morphological properties of each word in the sentence. We first produce silver standard parallel glossed data by automatically labeling the high-resource translation. The model then learns to transform source language morphological labels into output labels for the target language, mediated by a structured linguistic representation layer. We test the model on both low-resource and high-resource languages, and find that our simple CNN-based model achieves comparable performance to a state-of-the-art transformer-based model, at a fraction of the computational cost.", }

Linguistic analysis is a core task in the process of documenting, analyzing, and describing endangered and less-studied languages. In addition to providing insight into the properties of the language being studied, having tools to automatically label words in a language for grammatical category and morphological features can support a range of applications useful for language pedagogy and revitalization. At the same time, most modern NLP methods for these tasks require both large amounts of data in the language and compute costs well beyond the capacity of most research groups and language communities. In this paper, we present a gloss-to-gloss (g2g) model for linguistic analysis (specifically, morphological analysis and part-of-speech tagging) that is lightweight in terms of both data requirements and computational expense. The model is designed for the interlinear glossed text (IGT) format, in which we expect the source text of a sentence in a low-resource language, a translation of that sentence into a language of wider communication, and a detailed glossing of the morphological properties of each word in the sentence. We first produce silver standard parallel glossed data by automatically labeling the high-resource translation. The model then learns to transform source language morphological labels into output labels for the target language, mediated by a structured linguistic representation layer. We test the model on both low-resource and high-resource languages, and find that our simple CNN-based model achieves comparable performance to a state-of-the-art transformer-based model, at a fraction of the computational cost.

Lightweight morpheme labeling in context: Using structured linguistic representations to support linguistic analysis for the language documentation context

sigmorphon-1.9

Poster

https://aclanthology.org/2023.sigmorphon-1.10.bib

https://aclanthology.org/2023.sigmorphon-1.10/

@inproceedings{saunders-2023-improving, title = "Improving Automated Prediction of {E}nglish Lexical Blends Through the Use of Observable Linguistic Features", author = "Saunders, Jarem", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.10", doi = "10.18653/v1/2023.sigmorphon-1.10", pages = "93--97", abstract = "The process of lexical blending is difficult to reliably predict. This difficulty has been shown by machine learning approaches in blend modeling, including attempts using then state-of-the-art LSTM deep neural networks trained on character embeddings, which were able to predict lexical blends given the ordered constituent words in less than half of cases, at maximum. This project introduces a novel model architecture which dramatically increases the correct prediction rates for lexical blends, using only Polynomial regression and Random Forest models. This is achieved by generating multiple possible blend candidates for each input word pairing and evaluating them based on observable linguistic features. The success of this model architecture illustrates the potential usefulness of observable linguistic features for problems that elude more advanced models which utilize only features discovered in the latent space.", }

The process of lexical blending is difficult to reliably predict. This difficulty has been shown by machine learning approaches in blend modeling, including attempts using then state-of-the-art LSTM deep neural networks trained on character embeddings, which were able to predict lexical blends given the ordered constituent words in less than half of cases, at maximum. This project introduces a novel model architecture which dramatically increases the correct prediction rates for lexical blends, using only Polynomial regression and Random Forest models. This is achieved by generating multiple possible blend candidates for each input word pairing and evaluating them based on observable linguistic features. The success of this model architecture illustrates the potential usefulness of observable linguistic features for problems that elude more advanced models which utilize only features discovered in the latent space.

Improving Automated Prediction of English Lexical Blends Through the Use of Observable Linguistic Features

sigmorphon-1.10

Poster

https://aclanthology.org/2023.sigmorphon-1.11.bib

https://aclanthology.org/2023.sigmorphon-1.11/

@inproceedings{chen-bjerva-2023-colexifications, title = "Colexifications for Bootstrapping Cross-lingual Datasets: The Case of Phonology, Concreteness, and Affectiveness", author = "Chen, Yiyi and Bjerva, Johannes", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.11", doi = "10.18653/v1/2023.sigmorphon-1.11", pages = "98--109", abstract = "Colexification refers to the linguistic phenomenon where a single lexical form is used to convey multiple meanings. By studying cross-lingual colexifications, researchers have gained valuable insights into fields such as psycholinguistics and cognitive sciences (Jack- son et al., 2019; Xu et al., 2020; Karjus et al., 2021; Schapper and Koptjevskaja-Tamm, 2022; Fran{\~A}{\S}ois, 2022). While several multilingual colexification datasets exist, there is untapped potential in using this information to bootstrap datasets across such semantic features. In this paper, we aim to demonstrate how colexifications can be leveraged to create such cross-lingual datasets. We showcase curation procedures which result in a dataset covering 142 languages across 21 language families across the world. The dataset includes ratings of concreteness and affectiveness, mapped with phonemes and phonological features. We further analyze the dataset along different dimensions to demonstrate potential of the proposed procedures in facilitating further interdisciplinary research in psychology, cognitive science, and multilingual natural language processing (NLP). Based on initial investigations, we observe that i) colexifications that are closer in concreteness/affectiveness are more likely to colexify ; ii) certain initial/last phonemes are significantly correlated with concreteness/affectiveness intra language families, such as /k/ as the initial phoneme in both Turkic and Tai-Kadai correlated with concreteness, and /p/ in Dravidian and Sino-Tibetan correlated with Valence; iii) the type-to-token ratio (TTR) of phonemes are positively correlated with concreteness across several language families, while the length of phoneme segments are negatively correlated with concreteness; iv) certain phonological features are negatively correlated with concreteness across languages. The dataset is made public online for further research.", }

Colexification refers to the linguistic phenomenon where a single lexical form is used to convey multiple meanings. By studying cross-lingual colexifications, researchers have gained valuable insights into fields such as psycholinguistics and cognitive sciences (Jack- son et al., 2019; Xu et al., 2020; Karjus et al., 2021; Schapper and Koptjevskaja-Tamm, 2022; Fran{\~A}{\S}ois, 2022). While several multilingual colexification datasets exist, there is untapped potential in using this information to bootstrap datasets across such semantic features. In this paper, we aim to demonstrate how colexifications can be leveraged to create such cross-lingual datasets. We showcase curation procedures which result in a dataset covering 142 languages across 21 language families across the world. The dataset includes ratings of concreteness and affectiveness, mapped with phonemes and phonological features. We further analyze the dataset along different dimensions to demonstrate potential of the proposed procedures in facilitating further interdisciplinary research in psychology, cognitive science, and multilingual natural language processing (NLP). Based on initial investigations, we observe that i) colexifications that are closer in concreteness/affectiveness are more likely to colexify ; ii) certain initial/last phonemes are significantly correlated with concreteness/affectiveness intra language families, such as /k/ as the initial phoneme in both Turkic and Tai-Kadai correlated with concreteness, and /p/ in Dravidian and Sino-Tibetan correlated with Valence; iii) the type-to-token ratio (TTR) of phonemes are positively correlated with concreteness across several language families, while the length of phoneme segments are negatively correlated with concreteness; iv) certain phonological features are negatively correlated with concreteness across languages. The dataset is made public online for further research.

Colexifications for Bootstrapping Cross-lingual Datasets: The Case of Phonology, Concreteness, and Affectiveness

sigmorphon-1.11

Poster

2306.02646

https://aclanthology.org/2023.sigmorphon-1.12.bib

https://aclanthology.org/2023.sigmorphon-1.12/

@inproceedings{scherrer-2023-character, title = "Character alignment methods for dialect-to-standard normalization", author = "Scherrer, Yves", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.12", doi = "10.18653/v1/2023.sigmorphon-1.12", pages = "110--116", abstract = "This paper evaluates various character alignment methods on the task of sentence-level standardization of dialect transcriptions. We compare alignment methods from different scientific traditions (dialectometry, speech processing, machine translation) and apply them to Finnish, Norwegian and Swiss German dialect datasets. In the absence of gold alignments, we evaluate the methods on a set of characteristics that are deemed undesirable for the task. We find that trained alignment methods only show marginal benefits to simple Levenshtein distance. On this particular task, eflomal outperforms related methods such as GIZA++ or fast{\_}align by a large margin.", }

This paper evaluates various character alignment methods on the task of sentence-level standardization of dialect transcriptions. We compare alignment methods from different scientific traditions (dialectometry, speech processing, machine translation) and apply them to Finnish, Norwegian and Swiss German dialect datasets. In the absence of gold alignments, we evaluate the methods on a set of characteristics that are deemed undesirable for the task. We find that trained alignment methods only show marginal benefits to simple Levenshtein distance. On this particular task, eflomal outperforms related methods such as GIZA++ or fast{\_}align by a large margin.

Character alignment methods for dialect-to-standard normalization

sigmorphon-1.12

Poster

https://aclanthology.org/2023.sigmorphon-1.13.bib

https://aclanthology.org/2023.sigmorphon-1.13/

@inproceedings{goldman-etal-2023-sigmorphon, title = "{SIGMORPHON}{--}{U}ni{M}orph 2023 Shared Task 0: Typologically Diverse Morphological Inflection", author = "Goldman, Omer and Batsuren, Khuyagbaatar and Khalifa, Salam and Arora, Aryaman and Nicolai, Garrett and Tsarfaty, Reut and Vylomova, Ekaterina", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.13", doi = "10.18653/v1/2023.sigmorphon-1.13", pages = "117--125", abstract = "The 2023 SIGMORPHON{--}UniMorph shared task on typologically diverse morphological inflection included a wide range of languages: 26 languages from 9 primary language families. The data this year was all lemma-split, to allow testing models{'} generalization ability, and structured along the new hierarchical schema presented in (Batsuren et al., 2022). The systems submitted this year, 9 in number, showed ingenuity and innovativeness, including hard attention for explainability and bidirectional decoding. Special treatment was also given by many participants to the newly-introduced data in Japanese, due to the high abundance of unseen Kanji characters in its test set.", }

The 2023 SIGMORPHON{--}UniMorph shared task on typologically diverse morphological inflection included a wide range of languages: 26 languages from 9 primary language families. The data this year was all lemma-split, to allow testing models{'} generalization ability, and structured along the new hierarchical schema presented in (Batsuren et al., 2022). The systems submitted this year, 9 in number, showed ingenuity and innovativeness, including hard attention for explainability and bidirectional decoding. Special treatment was also given by many participants to the newly-introduced data in Japanese, due to the high abundance of unseen Kanji characters in its test set.

SIGMORPHON–UniMorph 2023 Shared Task 0: Typologically Diverse Morphological Inflection

sigmorphon-1.13

Poster

https://aclanthology.org/2023.sigmorphon-1.14.bib

https://aclanthology.org/2023.sigmorphon-1.14/

@inproceedings{breiss-jo-2023-sigmorphon, title = "{SIGMORPHON}{--}{U}ni{M}orph 2023 Shared Task 0, Part 2: Cognitively Plausible Morphophonological Generalization in {K}orean", author = "Breiss, Canaan and Jo, Jinyoung", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.14", doi = "10.18653/v1/2023.sigmorphon-1.14", pages = "126--131", abstract = "This paper summarises data collection and curation for Part 2 of the 2023 SIGMORPHON-UniMorph Shared Task 0, which focused on modeling speaker knowledge and generalization of a pair of interacting phonological processes in Korean. We briefly describe how modeling the generalization task could be of interest to researchers in both Natural Language Processing and linguistics, and then summarise the traditional description of the phonological processes that are at the center of the modeling challenge. We then describe the criteria we used to select and code cases of process application in two Korean speech corpora, which served as the primary learning data. We also report the technical details of the experiment we carried out that served as the primary test data.", }

This paper summarises data collection and curation for Part 2 of the 2023 SIGMORPHON-UniMorph Shared Task 0, which focused on modeling speaker knowledge and generalization of a pair of interacting phonological processes in Korean. We briefly describe how modeling the generalization task could be of interest to researchers in both Natural Language Processing and linguistics, and then summarise the traditional description of the phonological processes that are at the center of the modeling challenge. We then describe the criteria we used to select and code cases of process application in two Korean speech corpora, which served as the primary learning data. We also report the technical details of the experiment we carried out that served as the primary test data.

SIGMORPHON–UniMorph 2023 Shared Task 0, Part 2: Cognitively Plausible Morphophonological Generalization in Korean

sigmorphon-1.14

Poster

https://aclanthology.org/2023.sigmorphon-1.15.bib

https://aclanthology.org/2023.sigmorphon-1.15/

@inproceedings{kwak-etal-2023-morphological, title = "Morphological reinflection with weighted finite-state transducers", author = "Kwak, Alice and Hammond, Michael and Wing, Cheyenne", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.15", doi = "10.18653/v1/2023.sigmorphon-1.15", pages = "132--137", abstract = "This paper describes the submission by the University of Arizona to the SIGMORPHON 2023 Shared Task on typologically diverse morphological (re-)infection. In our submission, we investigate the role of frequency, length, and weighted transducers in addressing the challenge of morphological reinflection. We start with the non-neural baseline provided for the task and show how some improvement can be gained by integrating length and frequency in prefix selection. We also investigate using weighted finite-state transducers, jump-started from edit distance and directly augmented with frequency. Our specific technique is promising and quite simple, but we see only modest improvements for some languages here.", }

This paper describes the submission by the University of Arizona to the SIGMORPHON 2023 Shared Task on typologically diverse morphological (re-)infection. In our submission, we investigate the role of frequency, length, and weighted transducers in addressing the challenge of morphological reinflection. We start with the non-neural baseline provided for the task and show how some improvement can be gained by integrating length and frequency in prefix selection. We also investigate using weighted finite-state transducers, jump-started from edit distance and directly augmented with frequency. Our specific technique is promising and quite simple, but we see only modest improvements for some languages here.

Morphological reinflection with weighted finite-state transducers

sigmorphon-1.15

Poster

https://aclanthology.org/2023.sigmorphon-1.16.bib

https://aclanthology.org/2023.sigmorphon-1.16/

@inproceedings{jeong-etal-2023-linear, title = "Linear Discriminative Learning: a competitive non-neural baseline for morphological inflection", author = "Jeong, Cheonkam and Schmitz, Dominic and Kakolu Ramarao, Akhilesh and Stein, Anna and Tang, Kevin", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.16", doi = "10.18653/v1/2023.sigmorphon-1.16", pages = "138--150", abstract = "This paper presents our submission to the SIGMORPHON 2023 task 2 of Cognitively Plausible Morphophonological Generalization in Korean. We implemented both Linear Discriminative Learning and Transformer models and found that the Linear Discriminative Learning model trained on a combination of corpus and experimental data showed the best performance with the overall accuracy of around 83{\%}. We found that the best model must be trained on both corpus data and the experimental data of one particular participant. Our examination of speaker-variability and speaker-specific information did not explain why a particular participant combined well with the corpus data. We recommend Linear Discriminative Learning models as a future non-neural baseline system, owning to its training speed, accuracy, model interpretability and cognitive plausibility. In order to improve the model performance, we suggest using bigger data and/or performing data augmentation and incorporating speaker- and item-specifics considerably.", }

This paper presents our submission to the SIGMORPHON 2023 task 2 of Cognitively Plausible Morphophonological Generalization in Korean. We implemented both Linear Discriminative Learning and Transformer models and found that the Linear Discriminative Learning model trained on a combination of corpus and experimental data showed the best performance with the overall accuracy of around 83{\%}. We found that the best model must be trained on both corpus data and the experimental data of one particular participant. Our examination of speaker-variability and speaker-specific information did not explain why a particular participant combined well with the corpus data. We recommend Linear Discriminative Learning models as a future non-neural baseline system, owning to its training speed, accuracy, model interpretability and cognitive plausibility. In order to improve the model performance, we suggest using bigger data and/or performing data augmentation and incorporating speaker- and item-specifics considerably.

Linear Discriminative Learning: a competitive non-neural baseline for morphological inflection

sigmorphon-1.16

Poster

https://aclanthology.org/2023.sigmorphon-1.17.bib

https://aclanthology.org/2023.sigmorphon-1.17/

@inproceedings{girrbach-2023-tu, title = {T{\"u}-{CL} at {SIGMORPHON} 2023: Straight-Through Gradient Estimation for Hard Attention}, author = "Girrbach, Leander", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.17", doi = "10.18653/v1/2023.sigmorphon-1.17", pages = "151--165", abstract = "This paper describes our systems participating in the 2023 SIGMORPHON Shared Task on Morphological Inflection and in the 2023 SIGMORPHON Shared Task on Interlinear Glossing. We propose methods to enrich predictions from neural models with discrete, i.e. interpretable, information. For morphological inflection, our models learn deterministic mappings from subsets of source lemma characters and morphological tags to individual target characters, which introduces interpretability. For interlinear glossing, our models learn a shallow morpheme segmentation in an unsupervised way jointly with predicting glossing lines. Estimated segmentation may be useful when no ground-truth segmentation is available. As both methods introduce discreteness into neural models, our technical contribution is to show that straight-through gradient estimators are effective to train hard attention models.", }

This paper describes our systems participating in the 2023 SIGMORPHON Shared Task on Morphological Inflection and in the 2023 SIGMORPHON Shared Task on Interlinear Glossing. We propose methods to enrich predictions from neural models with discrete, i.e. interpretable, information. For morphological inflection, our models learn deterministic mappings from subsets of source lemma characters and morphological tags to individual target characters, which introduces interpretability. For interlinear glossing, our models learn a shallow morpheme segmentation in an unsupervised way jointly with predicting glossing lines. Estimated segmentation may be useful when no ground-truth segmentation is available. As both methods introduce discreteness into neural models, our technical contribution is to show that straight-through gradient estimators are effective to train hard attention models.

Tü-CL at SIGMORPHON 2023: Straight-Through Gradient Estimation for Hard Attention

sigmorphon-1.17

Poster

https://aclanthology.org/2023.sigmorphon-1.18.bib

https://aclanthology.org/2023.sigmorphon-1.18/

@inproceedings{astrach-pinter-2023-bgu, title = "The {BGU}-{M}e{L}e{L} System for the {SIGMORPHON} 2023 Shared Task on Morphological Inflection", author = "Astrach, Gal and Pinter, Yuval", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.18", doi = "10.18653/v1/2023.sigmorphon-1.18", pages = "166--170", abstract = "This paper presents the submission by the MeLeL team to the SIGMORPHON{--}UniMorph Shared Task on Typologically Diverse and Acquisition-Inspired Morphological Inflection Generation Part 3: Models of Acquisition of Inflectional Noun Morphology in Polish, Estonian, and Finnish. This task requires us to produce the word form given a lemma and a grammatical case, while trying to produce the same error-rate as in children. We approach this task with a reduced-size character-based transformer model, multilingual training and an upsampling method to introduce bias.", }

This paper presents the submission by the MeLeL team to the SIGMORPHON{--}UniMorph Shared Task on Typologically Diverse and Acquisition-Inspired Morphological Inflection Generation Part 3: Models of Acquisition of Inflectional Noun Morphology in Polish, Estonian, and Finnish. This task requires us to produce the word form given a lemma and a grammatical case, while trying to produce the same error-rate as in children. We approach this task with a reduced-size character-based transformer model, multilingual training and an upsampling method to introduce bias.

The BGU-MeLeL System for the SIGMORPHON 2023 Shared Task on Morphological Inflection

sigmorphon-1.18

Poster

https://aclanthology.org/2023.sigmorphon-1.19.bib

https://aclanthology.org/2023.sigmorphon-1.19/

@inproceedings{girrbach-2023-tu-cl, title = {T{\"u}-{CL} at {SIGMORPHON} 2023: Straight-Through Gradient Estimation for Hard Attention}, author = "Girrbach, Leander", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.19", doi = "10.18653/v1/2023.sigmorphon-1.19", pages = "171--185", abstract = "This paper describes our systems participating in the 2023 SIGMORPHON Shared Task on Morphological Inflection and in the 2023 SIGMORPHON Shared Task on Interlinear Glossing. We propose methods to enrich predictions from neural models with discrete, i.e. interpretable, information. For morphological inflection, our models learn deterministic mappings from subsets of source lemma characters and morphological tags to individual target characters, which introduces interpretability. For interlinear glossing, our models learn a shallow morpheme segmentation in an unsupervised way jointly with predicting glossing lines. Estimated segmentation may be useful when no ground-truth segmentation is available. As both methods introduce discreteness into neural models, our technical contribution is to show that straight-through gradient estimators are effective to train hard attention models.", }

This paper describes our systems participating in the 2023 SIGMORPHON Shared Task on Morphological Inflection and in the 2023 SIGMORPHON Shared Task on Interlinear Glossing. We propose methods to enrich predictions from neural models with discrete, i.e. interpretable, information. For morphological inflection, our models learn deterministic mappings from subsets of source lemma characters and morphological tags to individual target characters, which introduces interpretability. For interlinear glossing, our models learn a shallow morpheme segmentation in an unsupervised way jointly with predicting glossing lines. Estimated segmentation may be useful when no ground-truth segmentation is available. As both methods introduce discreteness into neural models, our technical contribution is to show that straight-through gradient estimators are effective to train hard attention models.

Tü-CL at SIGMORPHON 2023: Straight-Through Gradient Estimation for Hard Attention

sigmorphon-1.19

Poster

https://aclanthology.org/2023.sigmorphon-1.20.bib

https://aclanthology.org/2023.sigmorphon-1.20/

@inproceedings{ginn-etal-2023-findings, title = "Findings of the {SIGMORPHON} 2023 Shared Task on Interlinear Glossing", author = "Ginn, Michael and Moeller, Sarah and Palmer, Alexis and Stacey, Anna and Nicolai, Garrett and Hulden, Mans and Silfverberg, Miikka", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.20", doi = "10.18653/v1/2023.sigmorphon-1.20", pages = "186--201", abstract = {This paper presents the findings of the SIGMORPHON 2023 Shared Task on Interlinear Glossing. This first iteration of the shared task explores glossing of a set of six typologically diverse languages: Arapaho, Gitksan, Lezgi, Nat{\"u}gu, Tsez and Uspanteko. The shared task encompasses two tracks: a resource-scarce closed track and an open track, where participants are allowed to utilize external data resources. Five teams participated in the shared task. The winning team T{\"u}-CL achieved a 23.99{\%}-point improvement over a baseline RoBERTa system in the closed track and a 17.42{\%}-point improvement in the open track.}, }

This paper presents the findings of the SIGMORPHON 2023 Shared Task on Interlinear Glossing. This first iteration of the shared task explores glossing of a set of six typologically diverse languages: Arapaho, Gitksan, Lezgi, Nat{\"u}gu, Tsez and Uspanteko. The shared task encompasses two tracks: a resource-scarce closed track and an open track, where participants are allowed to utilize external data resources. Five teams participated in the shared task. The winning team T{\"u}-CL achieved a 23.99{\%}-point improvement over a baseline RoBERTa system in the closed track and a 17.42{\%}-point improvement in the open track.

Findings of the SIGMORPHON 2023 Shared Task on Interlinear Glossing

sigmorphon-1.20

Poster

https://aclanthology.org/2023.sigmorphon-1.21.bib

https://aclanthology.org/2023.sigmorphon-1.21/

@inproceedings{okabe-yvon-2023-lisn, title = "{LISN} @ {SIGMORPHON} 2023 Shared Task on Interlinear Glossing", author = "Okabe, Shu and Yvon, Fran{\c{c}}ois", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.21", doi = "10.18653/v1/2023.sigmorphon-1.21", pages = "202--208", abstract = "This paper describes LISN{''}{'}{``}s submission to the second track (open track) of the shared task on Interlinear Glossing for SIGMORPHON 2023. Our systems are based on Lost, a variation of linear Conditional Random Fields initially developed as a probabilistic translation model and then adapted to the glossing task. This model allows us to handle one of the main challenges posed by glossing, i.e. the fact that the list of potential labels for lexical morphemes is not fixed in advance and needs to be extended dynamically when labelling units are not seen in training. In such situations, we show how to make use of candidate lexical glosses found in the translation and discuss how such extension affects the training and inference procedures. The resulting automatic glossing systems prove to yield very competitive results, especially in low-resource settings.", }

This paper describes LISN{''}{'}{``}s submission to the second track (open track) of the shared task on Interlinear Glossing for SIGMORPHON 2023. Our systems are based on Lost, a variation of linear Conditional Random Fields initially developed as a probabilistic translation model and then adapted to the glossing task. This model allows us to handle one of the main challenges posed by glossing, i.e. the fact that the list of potential labels for lexical morphemes is not fixed in advance and needs to be extended dynamically when labelling units are not seen in training. In such situations, we show how to make use of candidate lexical glosses found in the translation and discuss how such extension affects the training and inference procedures. The resulting automatic glossing systems prove to yield very competitive results, especially in low-resource settings.

LISN @ SIGMORPHON 2023 Shared Task on Interlinear Glossing

sigmorphon-1.21

Poster

https://aclanthology.org/2023.sigmorphon-1.22.bib

https://aclanthology.org/2023.sigmorphon-1.22/

@inproceedings{he-etal-2023-sigmorefun, title = "{S}ig{M}ore{F}un Submission to the {SIGMORPHON} Shared Task on Interlinear Glossing", author = "He, Taiqi and Tjuatja, Lindia and Robinson, Nathaniel and Watanabe, Shinji and Mortensen, David R. and Neubig, Graham and Levin, Lori", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.22", doi = "10.18653/v1/2023.sigmorphon-1.22", pages = "209--216", abstract = "In our submission to the SIGMORPHON 2023 Shared Task on interlinear glossing (IGT), we explore approaches to data augmentation and modeling across seven low-resource languages. For data augmentation, we explore two approaches: creating artificial data from the provided training data and utilizing existing IGT resources in other languages. On the modeling side, we test an enhanced version of the provided token classification baseline as well as a pretrained multilingual seq2seq model. Additionally, we apply post-correction using a dictionary for Gitksan, the language with the smallest amount of data. We find that our token classification models are the best performing, with the highest word-level accuracy for Arapaho and highest morpheme-level accuracy for Gitksan out of all submissions. We also show that data augmentation is an effective strategy, though applying artificial data pretraining has very different effects across both models tested.", }

In our submission to the SIGMORPHON 2023 Shared Task on interlinear glossing (IGT), we explore approaches to data augmentation and modeling across seven low-resource languages. For data augmentation, we explore two approaches: creating artificial data from the provided training data and utilizing existing IGT resources in other languages. On the modeling side, we test an enhanced version of the provided token classification baseline as well as a pretrained multilingual seq2seq model. Additionally, we apply post-correction using a dictionary for Gitksan, the language with the smallest amount of data. We find that our token classification models are the best performing, with the highest word-level accuracy for Arapaho and highest morpheme-level accuracy for Gitksan out of all submissions. We also show that data augmentation is an effective strategy, though applying artificial data pretraining has very different effects across both models tested.

SigMoreFun Submission to the SIGMORPHON Shared Task on Interlinear Glossing

sigmorphon-1.22

Poster

https://aclanthology.org/2023.sigmorphon-1.23.bib

https://aclanthology.org/2023.sigmorphon-1.23/

@inproceedings{coates-2023-ensembled, title = "An Ensembled Encoder-Decoder System for Interlinear Glossed Text", author = "Coates, Edith", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.23", doi = "10.18653/v1/2023.sigmorphon-1.23", pages = "217--221", abstract = "This paper presents my submission to Track 1 of the 2023 SIGMORPHON shared task on interlinear glossed text (IGT). There are a wide amount of techniques for building and training IGT models (see Moeller and Hulden, 2018; McMillan-Major, 2020; Zhao et al., 2020). I describe my ensembled sequence-to-sequence approach, perform experiments, and share my submission{'}s test-set accuracy. I also discuss future areas of research in low-resource token classification methods for IGT.", }

This paper presents my submission to Track 1 of the 2023 SIGMORPHON shared task on interlinear glossed text (IGT). There are a wide amount of techniques for building and training IGT models (see Moeller and Hulden, 2018; McMillan-Major, 2020; Zhao et al., 2020). I describe my ensembled sequence-to-sequence approach, perform experiments, and share my submission{'}s test-set accuracy. I also discuss future areas of research in low-resource token classification methods for IGT.

An Ensembled Encoder-Decoder System for Interlinear Glossed Text

sigmorphon-1.23

Poster

https://aclanthology.org/2023.sigmorphon-1.24.bib

https://aclanthology.org/2023.sigmorphon-1.24/

@inproceedings{cross-etal-2023-glossy, title = "Glossy Bytes: Neural Glossing using Subword Encoding", author = "Cross, Ziggy and Yun, Michelle and Apparaju, Ananya and MacCabe, Jata and Nicolai, Garrett and Silfverberg, Miikka", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.24", doi = "10.18653/v1/2023.sigmorphon-1.24", pages = "222--229", abstract = "This paper presents several different neural subword modelling based approaches to interlinear glossing for seven under-resourced languages as a part of the 2023 SIGMORPHON shared task on interlinear glossing. We experiment with various augmentation and tokenization strategies for both the open and closed tracks of data. We found that while byte-level models may perform well for greater amounts of data, character based approaches remain competitive in their performance in lower resource settings.", }

This paper presents several different neural subword modelling based approaches to interlinear glossing for seven under-resourced languages as a part of the 2023 SIGMORPHON shared task on interlinear glossing. We experiment with various augmentation and tokenization strategies for both the open and closed tracks of data. We found that while byte-level models may perform well for greater amounts of data, character based approaches remain competitive in their performance in lower resource settings.

Glossy Bytes: Neural Glossing using Subword Encoding

sigmorphon-1.24

Poster

https://aclanthology.org/2023.sigmorphon-1.27.bib

https://aclanthology.org/2023.sigmorphon-1.27/

@inproceedings{mccarthy-etal-2023-sigmorphon, title = "The {SIGMORPHON} 2022 Shared Task on Cross-lingual and Low-Resource Grapheme-to-Phoneme Conversion", author = "McCarthy, Arya D. and Lee, Jackson L. and DeLucia, Alexandra and Bartley, Travis and Agarwal, Milind and Ashby, Lucas F.E. and Del Signore, Luca and Gibson, Cameron and Raff, Reuben and Wu, Winston", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.27", doi = "10.18653/v1/2023.sigmorphon-1.27", pages = "230--238", abstract = "Grapheme-to-phoneme conversion is an important component in many speech technologies, but until recently there were no multilingual benchmarks for this task. The third iteration of the SIGMORPHON shared task on multilingual grapheme-to-phoneme conversion features many improvements from the previous year{'}s task (Ashby et al., 2021), including additional languages, three subtasks varying the amount of available resources, extensive quality assurance procedures, and automated error analyses. Three teams submitted a total of fifteen systems, at best achieving relative reductions of word error rate of 14{\%} in the crosslingual subtask and 14{\%} in the very-low resource subtask. The generally consistent result is that cross-lingual transfer substantially helps grapheme-to-phoneme modeling, but not to the same degree as in-language examples.", }

Grapheme-to-phoneme conversion is an important component in many speech technologies, but until recently there were no multilingual benchmarks for this task. The third iteration of the SIGMORPHON shared task on multilingual grapheme-to-phoneme conversion features many improvements from the previous year{'}s task (Ashby et al., 2021), including additional languages, three subtasks varying the amount of available resources, extensive quality assurance procedures, and automated error analyses. Three teams submitted a total of fifteen systems, at best achieving relative reductions of word error rate of 14{\%} in the crosslingual subtask and 14{\%} in the very-low resource subtask. The generally consistent result is that cross-lingual transfer substantially helps grapheme-to-phoneme modeling, but not to the same degree as in-language examples.

The SIGMORPHON 2022 Shared Task on Cross-lingual and Low-Resource Grapheme-to-Phoneme Conversion

sigmorphon-1.27

Poster

https://aclanthology.org/2023.sigmorphon-1.28.bib

https://aclanthology.org/2023.sigmorphon-1.28/

@inproceedings{girrbach-2023-sigmorphon, title = "{SIGMORPHON} 2022 Shared Task on Grapheme-to-Phoneme Conversion Submission Description: Sequence Labelling for {G}2{P}", author = "Girrbach, Leander", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.28", doi = "10.18653/v1/2023.sigmorphon-1.28", pages = "239--244", abstract = "This paper describes our participation in the Third SIGMORPHON Shared Task on Grapheme-to-Phoneme Conversion (Low-Resource and Cross-Lingual) (McCarthy et al.,2022). Our models rely on different sequence labelling methods. The main model predicts multiple phonemes from each grapheme and is trained using CTC loss (Graves et al., 2006). We find that sequence labelling methods yield worse performance than the baseline when enough data is available, but can still be used when very little data is available. Furthermore, we demonstrate that alignments learned by the sequence labelling models can be easily inspected.", }

This paper describes our participation in the Third SIGMORPHON Shared Task on Grapheme-to-Phoneme Conversion (Low-Resource and Cross-Lingual) (McCarthy et al.,2022). Our models rely on different sequence labelling methods. The main model predicts multiple phonemes from each grapheme and is trained using CTC loss (Graves et al., 2006). We find that sequence labelling methods yield worse performance than the baseline when enough data is available, but can still be used when very little data is available. Furthermore, we demonstrate that alignments learned by the sequence labelling models can be easily inspected.

SIGMORPHON 2022 Shared Task on Grapheme-to-Phoneme Conversion Submission Description: Sequence Labelling for G2P

sigmorphon-1.28

Poster

https://aclanthology.org/2023.sigmorphon-1.29.bib

https://aclanthology.org/2023.sigmorphon-1.29/

@inproceedings{hammond-2023-low, title = "Low-resource grapheme-to-phoneme mapping with phonetically-conditioned transfer", author = "Hammond, Michael", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.29", doi = "10.18653/v1/2023.sigmorphon-1.29", pages = "245--248", abstract = "In this paper we explore a very simple nonneural approach to mapping orthography to phonetic transcription in a low-resource context with transfer data from a related language. We start from a baseline system and focus our efforts on data augmentation. We make three principal moves. First, we start with an HMMbased system (Novak et al., 2012). Second, we augment our basic system by recombining legal substrings in restricted fashion (Ryan and Hulden, 2020). Finally, we limit our transfer data by only using training pairs where the phonetic form shares all bigrams with the target language.", }

In this paper we explore a very simple nonneural approach to mapping orthography to phonetic transcription in a low-resource context with transfer data from a related language. We start from a baseline system and focus our efforts on data augmentation. We make three principal moves. First, we start with an HMMbased system (Novak et al., 2012). Second, we augment our basic system by recombining legal substrings in restricted fashion (Ryan and Hulden, 2020). Finally, we limit our transfer data by only using training pairs where the phonetic form shares all bigrams with the target language.

Low-resource grapheme-to-phoneme mapping with phonetically-conditioned transfer

sigmorphon-1.29

Poster

https://aclanthology.org/2023.sigmorphon-1.30.bib

https://aclanthology.org/2023.sigmorphon-1.30/

@inproceedings{lin-2023-future, title = "A future for universal grapheme-phoneme transduction modeling with neuralized finite-state transducers", author = "Lin, Chu-Cheng Lin", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.30", doi = "10.18653/v1/2023.sigmorphon-1.30", pages = "249--249", abstract = "We propose a universal grapheme-phoneme transduction model using neuralized finite-state transducers. Many computational models of grapheme-phoneme transduction nowadays are based on the (autoregressive) sequence-to-sequence string transduction paradigm. While such models have achieved state-of-the-art performance, they suffer from theoretical limitations of autoregressive models. On the other hand, neuralized finite-state transducers (NFSTs) have shown promising results on various string transduction tasks. NFSTs can be seen as a generalization of weighted finite-state transducers (WFSTs), and can be seen as pairs of a featurized finite-state machine ({`}marked finite-state transducer{'} or MFST in NFST terminology), and a string scoring function. Instead of taking a product of local contextual feature weights on FST arcs, NFSTs can employ arbitrary scoring functions to weight global contextual features of a string transduction, and therefore break the Markov property. Furthermore, NFSTs can be formally shown to be more expressive than (autoregressive) seq2seq models. Empirically, joint grapheme-phoneme transduction NFSTs have consistently outperformed vanilla seq2seq models on grapheme-tophoneme and phoneme-to-grapheme transduction tasks for English. Furthermore, they provide interpretable aligned string transductions, thanks to their finite-state machine component. In this talk, we propose a multilingual extension of the joint grapheme-phoneme NFST. We achieve this goal by modeling typological and phylogenetic features of languages and scripts as optional latent variables using a finite-state machine. The result is a versatile graphemephoneme transduction model: in addition to standard monolingual and multilingual transduction, the proposed multilingual NFST can also be used in various controlled generation scenarios, such as phoneme-to-grapheme transduction of an unseen language-script pair. We also plan to release an NFST software package.", }

We propose a universal grapheme-phoneme transduction model using neuralized finite-state transducers. Many computational models of grapheme-phoneme transduction nowadays are based on the (autoregressive) sequence-to-sequence string transduction paradigm. While such models have achieved state-of-the-art performance, they suffer from theoretical limitations of autoregressive models. On the other hand, neuralized finite-state transducers (NFSTs) have shown promising results on various string transduction tasks. NFSTs can be seen as a generalization of weighted finite-state transducers (WFSTs), and can be seen as pairs of a featurized finite-state machine ({`}marked finite-state transducer{'} or MFST in NFST terminology), and a string scoring function. Instead of taking a product of local contextual feature weights on FST arcs, NFSTs can employ arbitrary scoring functions to weight global contextual features of a string transduction, and therefore break the Markov property. Furthermore, NFSTs can be formally shown to be more expressive than (autoregressive) seq2seq models. Empirically, joint grapheme-phoneme transduction NFSTs have consistently outperformed vanilla seq2seq models on grapheme-tophoneme and phoneme-to-grapheme transduction tasks for English. Furthermore, they provide interpretable aligned string transductions, thanks to their finite-state machine component. In this talk, we propose a multilingual extension of the joint grapheme-phoneme NFST. We achieve this goal by modeling typological and phylogenetic features of languages and scripts as optional latent variables using a finite-state machine. The result is a versatile graphemephoneme transduction model: in addition to standard monolingual and multilingual transduction, the proposed multilingual NFST can also be used in various controlled generation scenarios, such as phoneme-to-grapheme transduction of an unseen language-script pair. We also plan to release an NFST software package.

A future for universal grapheme-phoneme transduction modeling with neuralized finite-state transducers

sigmorphon-1.30

Poster

https://aclanthology.org/2023.sigmorphon-1.31.bib

https://aclanthology.org/2023.sigmorphon-1.31/

@inproceedings{garrette-2023-fine, title = "Fine-tuning m{SLAM} for the {SIGMORPHON} 2022 Shared Task on Grapheme-to-Phoneme Conversion", author = "Garrette, Dan", editor = {Nicolai, Garrett and Chodroff, Eleanor and Mailhot, Frederic and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.31", doi = "10.18653/v1/2023.sigmorphon-1.31", pages = "250--250", abstract = "Grapheme-to-phoneme (G2P) conversion is a task that is inherently related to both written and spoken language. Therefore, our submission to the G2P shared task builds off of mSLAM (Bapna et al., 2022), a 600M parameter encoder model pretrained simultaneously on text from 101 languages and speech from 51 languages. For fine-tuning a G2P model, we combined mSLAM{'}s text encoder, which uses characters as its input tokens, with an uninitialized single-layer RNN-T decoder (Graves, 2012) whose vocabulary is the set of all 381 phonemes appearing in the shared task data. We took an explicitly multilingual approach to modeling the G2P tasks, fine-tuning and evaluating a single model that covered all the languages in each task, and adding language codes as prefixes to the input strings as a means of specifying the language of each example. Our models perform well in the shared task{'}s {``}high{''} setting (in which they were trained on 1,000 words from each language), though they do poorly in the {``}low{''} task setting (training on only 100 words from each language). Our models also perform reasonably in the {``}mixed{''} setting (training on 100 words in the target language and 1000 words in a related language), hinting that mSLAM{'}s multilingual pretraining may be enabling useful cross-lingual sharing.", }

Grapheme-to-phoneme (G2P) conversion is a task that is inherently related to both written and spoken language. Therefore, our submission to the G2P shared task builds off of mSLAM (Bapna et al., 2022), a 600M parameter encoder model pretrained simultaneously on text from 101 languages and speech from 51 languages. For fine-tuning a G2P model, we combined mSLAM{'}s text encoder, which uses characters as its input tokens, with an uninitialized single-layer RNN-T decoder (Graves, 2012) whose vocabulary is the set of all 381 phonemes appearing in the shared task data. We took an explicitly multilingual approach to modeling the G2P tasks, fine-tuning and evaluating a single model that covered all the languages in each task, and adding language codes as prefixes to the input strings as a means of specifying the language of each example. Our models perform well in the shared task{'}s {``}high{''} setting (in which they were trained on 1,000 words from each language), though they do poorly in the {``}low{''} task setting (training on only 100 words from each language). Our models also perform reasonably in the {``}mixed{''} setting (training on 100 words in the target language and 1000 words in a related language), hinting that mSLAM{'}s multilingual pretraining may be enabling useful cross-lingual sharing.

Fine-tuning mSLAM for the SIGMORPHON 2022 Shared Task on Grapheme-to-Phoneme Conversion

sigmorphon-1.31

Poster

https://aclanthology.org/2023.starsem-1.1.bib

https://aclanthology.org/2023.starsem-1.1/

@inproceedings{viegas-etal-2023-including, title = "Including Facial Expressions in Contextual Embeddings for Sign Language Generation", author = "Viegas, Carla and Inan, Mert and Quandt, Lorna and Alikhani, Malihe", editor = "Palmer, Alexis and Camacho-collados, Jose", booktitle = "Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.starsem-1.1", doi = "10.18653/v1/2023.starsem-1.1", pages = "1--10", abstract = "State-of-the-art sign language generation frameworks lack expressivity and naturalness which is the result of only focusing manual signs, neglecting the affective, grammatical and semantic functions of facial expressions. The purpose of this work is to augment semantic representation of sign language through grounding facial expressions. We study the effect of modeling the relationship between text, gloss, and facial expressions on the performance of the sign generation systems. In particular, we propose a Dual Encoder Transformer able to generate manual signs as well as facial expressions by capturing the similarities and differences found in text and sign gloss annotation. We take into consideration the role of facial muscle activity to express intensities of manual signs by being the first to employ facial action units in sign language generation. We perform a series of experiments showing that our proposed model improves the quality of automatically generated sign language.", }

State-of-the-art sign language generation frameworks lack expressivity and naturalness which is the result of only focusing manual signs, neglecting the affective, grammatical and semantic functions of facial expressions. The purpose of this work is to augment semantic representation of sign language through grounding facial expressions. We study the effect of modeling the relationship between text, gloss, and facial expressions on the performance of the sign generation systems. In particular, we propose a Dual Encoder Transformer able to generate manual signs as well as facial expressions by capturing the similarities and differences found in text and sign gloss annotation. We take into consideration the role of facial muscle activity to express intensities of manual signs by being the first to employ facial action units in sign language generation. We perform a series of experiments showing that our proposed model improves the quality of automatically generated sign language.

Including Facial Expressions in Contextual Embeddings for Sign Language Generation

starsem-1.1

Poster

2202.05383