@inproceedings{gan-etal-2023-exploiting,
title = "Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction",
author = "Gan, Leilei and
Li, Baokui and
Kuang, Kun and
Zhang, Yating and
Wang, Lei and
Luu, Anh and
Yang, Yi and
Wu, Fei",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.814",
doi = "10.18653/v1/2023.findings-emnlp.814",
pages = "12174--12185",
abstract = "Given the fact description text of a legal case, legal judgment prediction (LJP) aims to predict the case{'}s charge, applicable law article, and term of penalty. A core problem of LJP is distinguishing confusing legal cases where only subtle text differences exist. Previous studies fail to distinguish different classification errors with a standard cross-entropy classification loss and ignore the numbers in the fact description for predicting the term of penalty. To tackle these issues, in this work, first, in order to exploit the numbers in legal cases for predicting the term of penalty of certain charges, we enhance the representation of the fact description with extracted crime amounts which are encoded by a pre-trained numeracy model. Second, we propose a moco-based supervised contrastive learning to learn distinguishable representations and explore the best strategy to construct positive example pairs to benefit all three subtasks of LJP simultaneously. Extensive experiments on real-world datasets show that the proposed method achieves new state-of-the-art results, particularly for confusing legal cases. Ablation studies also demonstrate the effectiveness of each component.",
}
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<abstract>Given the fact description text of a legal case, legal judgment prediction (LJP) aims to predict the case’s charge, applicable law article, and term of penalty. A core problem of LJP is distinguishing confusing legal cases where only subtle text differences exist. Previous studies fail to distinguish different classification errors with a standard cross-entropy classification loss and ignore the numbers in the fact description for predicting the term of penalty. To tackle these issues, in this work, first, in order to exploit the numbers in legal cases for predicting the term of penalty of certain charges, we enhance the representation of the fact description with extracted crime amounts which are encoded by a pre-trained numeracy model. Second, we propose a moco-based supervised contrastive learning to learn distinguishable representations and explore the best strategy to construct positive example pairs to benefit all three subtasks of LJP simultaneously. Extensive experiments on real-world datasets show that the proposed method achieves new state-of-the-art results, particularly for confusing legal cases. Ablation studies also demonstrate the effectiveness of each component.</abstract>
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%0 Conference Proceedings
%T Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction
%A Gan, Leilei
%A Li, Baokui
%A Kuang, Kun
%A Zhang, Yating
%A Wang, Lei
%A Luu, Anh
%A Yang, Yi
%A Wu, Fei
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F gan-etal-2023-exploiting
%X Given the fact description text of a legal case, legal judgment prediction (LJP) aims to predict the case’s charge, applicable law article, and term of penalty. A core problem of LJP is distinguishing confusing legal cases where only subtle text differences exist. Previous studies fail to distinguish different classification errors with a standard cross-entropy classification loss and ignore the numbers in the fact description for predicting the term of penalty. To tackle these issues, in this work, first, in order to exploit the numbers in legal cases for predicting the term of penalty of certain charges, we enhance the representation of the fact description with extracted crime amounts which are encoded by a pre-trained numeracy model. Second, we propose a moco-based supervised contrastive learning to learn distinguishable representations and explore the best strategy to construct positive example pairs to benefit all three subtasks of LJP simultaneously. Extensive experiments on real-world datasets show that the proposed method achieves new state-of-the-art results, particularly for confusing legal cases. Ablation studies also demonstrate the effectiveness of each component.
%R 10.18653/v1/2023.findings-emnlp.814
%U https://aclanthology.org/2023.findings-emnlp.814
%U https://doi.org/10.18653/v1/2023.findings-emnlp.814
%P 12174-12185
Markdown (Informal)
[Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction](https://aclanthology.org/2023.findings-emnlp.814) (Gan et al., Findings 2023)
ACL