@inproceedings{shi-etal-2024-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2024 Task 5: Regularized Legal-{BERT} for Legal Argument Reasoning Task in Civil Procedure",
    author = "Shi, Peng  and
      Wang, Jin  and
      Zhang, Xuejie",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Tayyar Madabushi, Harish  and
      Da San Martino, Giovanni  and
      Rosenthal, Sara  and
      Ros{\'a}, Aiala},
    booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.semeval-1.108/",
    doi = "10.18653/v1/2024.semeval-1.108",
    pages = "757--762",
    abstract = "This paper describes the submission of team YNU-HPCC to SemEval-2024 for Task 5: The Legal Argument Reasoning Task in Civil Procedure. The task asks candidates the topic, questions, and answers, classifying whether a given candidate`s answer is correct (True) or incorrect (False). To make a sound judgment, we propose a system. This system is based on fine-tuning the Legal-BERT model that specializes in solving legal problems. Meanwhile,Regularized Dropout (R-Drop) and focal Loss were used in the model. R-Drop is used for data augmentation, and focal loss addresses data imbalances. Our system achieved relatively good results on the competition`s official leaderboard. The code of this paper is available at https://github.com/YNU-PengShi/SemEval-2024-Task5."
}