@inproceedings{lamsiyah-etal-2023-ul,
    title = "{UL} {\&} {UM}6{P} at {S}em{E}val-2023 Task 10: Semi-Supervised Multi-task Learning for Explainable Detection of Online Sexism",
    author = "Lamsiyah, Salima  and
      El Mahdaouy, Abdelkader  and
      Alami, Hamza  and
      Berrada, Ismail  and
      Schommer, Christoph",
    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-1.88",
    doi = "10.18653/v1/2023.semeval-1.88",
    pages = "644--650",
    abstract = "This paper introduces our participating system to the Explainable Detection of Online Sexism (EDOS) SemEval-2023 - Task 10: Explainable Detection of Online Sexism. The EDOS shared task covers three hierarchical sub-tasks for sexism detection, coarse-grained and fine-grained categorization. We have investigated both single-task and multi-task learning based on RoBERTa transformer-based language models. For improving the results, we have performed further pre-training of RoBERTa on the provided unlabeled data. Besides, we have employed a small sample of the unlabeled data for semi-supervised learning using the minimum class-confusion loss. Our system has achieved macro F1 scores of 82.25{\textbackslash}{\%}, 67.35{\textbackslash}{\%}, and 49.8{\textbackslash}{\%} on Tasks A, B, and C, respectively.",
}

This paper introduces our participating system to the Explainable Detection of Online Sexism (EDOS) SemEval-2023 - Task 10: Explainable Detection of Online Sexism. The EDOS shared task covers three hierarchical sub-tasks for sexism detection, coarse-grained and fine-grained categorization. We have investigated both single-task and multi-task learning based on RoBERTa transformer-based language models. For improving the results, we have performed further pre-training of RoBERTa on the provided unlabeled data. Besides, we have employed a small sample of the unlabeled data for semi-supervised learning using the minimum class-confusion loss. Our system has achieved macro F1 scores of 82.25{\textbackslash}{\%}, 67.35{\textbackslash}{\%}, and 49.8{\textbackslash}{\%} on Tasks A, B, and C, respectively.
Lamsiyah, Salima El Mahdaouy, Abdelkader Alami, Hamza Berrada, Ismail Schommer, Christoph
UL & UM6P at SemEval-2023 Task 10: Semi-Supervised Multi-task Learning for Explainable Detection of Online Sexism