@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T YNU-HPCC at SemEval-2024 Task 5: Regularized Legal-BERT for Legal Argument Reasoning Task in Civil Procedure
%A Shi, Peng
%A Wang, Jin
%A Zhang, Xuejie
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F shi-etal-2024-ynu
%X 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.
%R 10.18653/v1/2024.semeval-1.108
%U https://aclanthology.org/2024.semeval-1.108
%U https://doi.org/10.18653/v1/2024.semeval-1.108
%P 757-762
Markdown (Informal)
[YNU-HPCC at SemEval-2024 Task 5: Regularized Legal-BERT for Legal Argument Reasoning Task in Civil Procedure](https://aclanthology.org/2024.semeval-1.108) (Shi et al., SemEval 2024)
ACL