@inproceedings{pu-zhou-2023-pcj,
    title = "{PCJ} at {S}em{E}val-2023 Task 10: A Ensemble Model Based on Pre-trained Model for Sexism Detection and Classification in {E}nglish",
    author = "Pu, Chujun  and
      Zhou, Xiaobing",
    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-1.59",
    doi = "10.18653/v1/2023.semeval-1.59",
    pages = "433--438",
    abstract = "This paper describes the system and the resulting model submitted by our team {``}PCJ{''} to the SemEval-2023 Task 10 sub-task A contest. In this task, we need to test the English text content in the posts to determine whether there is sexism, which involves emotional text classification. Our submission system utilizes methods based on RoBERTa, SimCSE-RoBERTa pre-training models, and model ensemble to classify and train on datasets provided by the organizers. In the final assessment, our submission achieved a macro average F1 score of 0.8449, ranking 28th out of 84 teams in Task A.",
}

This paper describes the system and the resulting model submitted by our team {``}PCJ{''} to the SemEval-2023 Task 10 sub-task A contest. In this task, we need to test the English text content in the posts to determine whether there is sexism, which involves emotional text classification. Our submission system utilizes methods based on RoBERTa, SimCSE-RoBERTa pre-training models, and model ensemble to classify and train on datasets provided by the organizers. In the final assessment, our submission achieved a macro average F1 score of 0.8449, ranking 28th out of 84 teams in Task A.
Pu, Chujun Zhou, Xiaobing
PCJ at SemEval-2023 Task 10: A Ensemble Model Based on Pre-trained Model for Sexism Detection and Classification in English