@inproceedings{pan-etal-2023-umuteam,
    title = "{UMUT}eam at {S}em{E}val-2023 Task 10: Fine-grained detection of sexism in {E}nglish",
    author = "Pan, Ronghao  and
      Garc{\'\i}a-D{\'\i}az, Jos{\'e} Antonio  and
      Jim{\'e}nez Zafra, Salud Mar{\'\i}a  and
      Valencia-Garc{\'\i}a, Rafael",
    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-1.80",
    doi = "10.18653/v1/2023.semeval-1.80",
    pages = "589--594",
    abstract = "In this manuscript, we describe the participation of UMUTeam in the Explainable Detection of Online Sexism shared task proposed at SemEval 2023. This task concerns the precise and explainable detection of sexist content on Gab and Reddit, i.e., developing detailed classifiers that not only identify what is sexist, but also explain why it is sexism. Our participation in the three EDOS subtasks is based on extending new unlabeled sexism data in the Masked Language Model task of a pre-trained model, such as RoBERTa-large to improve its generalization capacity and its performance on classification tasks. Once the model has been pre-trained with the new data, fine-tuning of this model is performed for different specific sexism classification tasks. Our system has achieved excellent results in this competitive task, reaching top 24 (84) in Task A, top 23 (69) in Task B, and top 13 (63) in Task C.",
}

In this manuscript, we describe the participation of UMUTeam in the Explainable Detection of Online Sexism shared task proposed at SemEval 2023. This task concerns the precise and explainable detection of sexist content on Gab and Reddit, i.e., developing detailed classifiers that not only identify what is sexist, but also explain why it is sexism. Our participation in the three EDOS subtasks is based on extending new unlabeled sexism data in the Masked Language Model task of a pre-trained model, such as RoBERTa-large to improve its generalization capacity and its performance on classification tasks. Once the model has been pre-trained with the new data, fine-tuning of this model is performed for different specific sexism classification tasks. Our system has achieved excellent results in this competitive task, reaching top 24 (84) in Task A, top 23 (69) in Task B, and top 13 (63) in Task C.
Pan, Ronghao Garc{\'\i}a-D{\'\i}az, Jos{\'e} Antonio Jim{\'e}nez Zafra, Salud Mar{\'\i}a Valencia-Garc{\'\i}a, Rafael
UMUTeam at SemEval-2023 Task 10: Fine-grained detection of sexism in English