@inproceedings{sun-zhou-2024-ignore,
title = "ignore at {S}em{E}val-2024 Task 5: A Legal Classification Model with Summary Generation and Contrastive Learning",
author = "Sun, Binjie and
Zhou, Xiaobing",
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-1.80",
doi = "10.18653/v1/2024.semeval-1.80",
pages = "530--535",
abstract = "This paper describes our work for SemEval-2024 Task 5: The Legal Argument Reasoning Task in Civil Procedure. After analyzing the task requirements and the training dataset, we used data augmentation, adopted the large model GPT for summary generation, and added supervised contrastive learning to the basic BERT model. Our system achieved an F1 score of 0.551, ranking 14th in the competition leaderboard. Our system achieves an F1 score improvement of 0.1241 over the official baseline model.",
}
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<abstract>This paper describes our work for SemEval-2024 Task 5: The Legal Argument Reasoning Task in Civil Procedure. After analyzing the task requirements and the training dataset, we used data augmentation, adopted the large model GPT for summary generation, and added supervised contrastive learning to the basic BERT model. Our system achieved an F1 score of 0.551, ranking 14th in the competition leaderboard. Our system achieves an F1 score improvement of 0.1241 over the official baseline model.</abstract>
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%0 Conference Proceedings
%T ignore at SemEval-2024 Task 5: A Legal Classification Model with Summary Generation and Contrastive Learning
%A Sun, Binjie
%A Zhou, Xiaobing
%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 sun-zhou-2024-ignore
%X This paper describes our work for SemEval-2024 Task 5: The Legal Argument Reasoning Task in Civil Procedure. After analyzing the task requirements and the training dataset, we used data augmentation, adopted the large model GPT for summary generation, and added supervised contrastive learning to the basic BERT model. Our system achieved an F1 score of 0.551, ranking 14th in the competition leaderboard. Our system achieves an F1 score improvement of 0.1241 over the official baseline model.
%R 10.18653/v1/2024.semeval-1.80
%U https://aclanthology.org/2024.semeval-1.80
%U https://doi.org/10.18653/v1/2024.semeval-1.80
%P 530-535
Markdown (Informal)
[ignore at SemEval-2024 Task 5: A Legal Classification Model with Summary Generation and Contrastive Learning](https://aclanthology.org/2024.semeval-1.80) (Sun & Zhou, SemEval 2024)
ACL