@inproceedings{kruff-tran-2023-billie,
    title = "Billie-Newman at {S}em{E}val-2023 Task 5: Clickbait Classification and Question Answering with Pre-Trained Language Models, Named Entity Recognition and Rule-Based Approaches",
    author = "Kruff, Andreas  and
      Tran, Anh Huy",
    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-1.213",
    doi = "10.18653/v1/2023.semeval-1.213",
    pages = "1542--1550",
    abstract = "In this paper, we describe the implementations of our systems for the SemEval-2023 Task 5 {`}Clickbait Spoiling{'}, which involves the classification of clickbait posts in sub-task 1 and the spoiler generation and question answering of clickbait posts in sub-task 2, ultimately achieving a balanced accuracy of 0.593 and a BLEU score of 0.322 on the test datasets in sub-task 1 and sub-task 2 respectively. For this, we propose the usage of RoBERTa transformer models and modify them for each specific downstream task. In sub-task 1, we use the pre-trained RoBERTa model and use it in conjunction with NER, a spoiler-title ratio, a regex check for enumerations and lists and the use of input reformulation. In sub-task 2, we propose the usage of the RoBERTa-SQuAD2.0 model for extractive question answering in combination with a contextual rule-based approach for multi-type spoilers in order to generate spoiler answers.",
}

In this paper, we describe the implementations of our systems for the SemEval-2023 Task 5 {`}Clickbait Spoiling{'}, which involves the classification of clickbait posts in sub-task 1 and the spoiler generation and question answering of clickbait posts in sub-task 2, ultimately achieving a balanced accuracy of 0.593 and a BLEU score of 0.322 on the test datasets in sub-task 1 and sub-task 2 respectively. For this, we propose the usage of RoBERTa transformer models and modify them for each specific downstream task. In sub-task 1, we use the pre-trained RoBERTa model and use it in conjunction with NER, a spoiler-title ratio, a regex check for enumerations and lists and the use of input reformulation. In sub-task 2, we propose the usage of the RoBERTa-SQuAD2.0 model for extractive question answering in combination with a contextual rule-based approach for multi-type spoilers in order to generate spoiler answers.
Kruff, Andreas Tran, Anh Huy
Billie-Newman at SemEval-2023 Task 5: Clickbait Classification and Question Answering with Pre-Trained Language Models, Named Entity Recognition and Rule-Based Approaches