@inproceedings{christodoulou-2023-nlp,
    title = "{NLP}{\_}{CHRISTINE} at {S}em{E}val-2023 Task 10: Utilizing Transformer Contextual Representations and Ensemble Learning for Sexism Detection on Social Media Texts",
    author = "Christodoulou, Christina",
    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-1.81",
    doi = "10.18653/v1/2023.semeval-1.81",
    pages = "595--602",
    abstract = "The paper describes the SemEval-2023 Task 10: {``}Explainable Detection of Online Sexism (EDOS){''}, which investigates the detection of sexism on two social media sites, Gab and Reddit, by encouraging the development of machine learning models that perform binary and multi-class classification on English texts. The EDOS Task consisted of three hierarchical sub-tasks: binary sexism detection in sub-task A, category of sexism detection in sub-task B and fine-grained vector of sexism detection in sub-task C. My participation in EDOS comprised fine-tuning of different layer representations of Transformer-based pre-trained language models, namely BERT, AlBERT and RoBERTa, and ensemble learning via majority voting of the best performing models. Despite the low rank mainly due to a submission error, the system employed the largest version of the aforementioned Transformer models (BERT-Large, ALBERT-XXLarge-v1, ALBERT-XXLarge-v2, RoBERTa-Large), experimented with their multi-layer structure and aggregated their predictions so as to get the final result. My predictions on the test sets achieved 82.88{\%}, 63.77{\%} and 43.08{\%} Macro-F1 score in sub-tasks A, B and C respectively.",
}

The paper describes the SemEval-2023 Task 10: {``}Explainable Detection of Online Sexism (EDOS){''}, which investigates the detection of sexism on two social media sites, Gab and Reddit, by encouraging the development of machine learning models that perform binary and multi-class classification on English texts. The EDOS Task consisted of three hierarchical sub-tasks: binary sexism detection in sub-task A, category of sexism detection in sub-task B and fine-grained vector of sexism detection in sub-task C. My participation in EDOS comprised fine-tuning of different layer representations of Transformer-based pre-trained language models, namely BERT, AlBERT and RoBERTa, and ensemble learning via majority voting of the best performing models. Despite the low rank mainly due to a submission error, the system employed the largest version of the aforementioned Transformer models (BERT-Large, ALBERT-XXLarge-v1, ALBERT-XXLarge-v2, RoBERTa-Large), experimented with their multi-layer structure and aggregated their predictions so as to get the final result. My predictions on the test sets achieved 82.88{\%}, 63.77{\%} and 43.08{\%} Macro-F1 score in sub-tasks A, B and C respectively.
Christodoulou, Christina
NLP_CHRISTINE at SemEval-2023 Task 10: Utilizing Transformer Contextual Representations and Ensemble Learning for Sexism Detection on Social Media Texts