Augmenting Legal Judgment Prediction with Contrastive Case Relations

Dugang Liu, Weihao Du, Lei Li, Weike Pan, Zhong Ming


Abstract
Existing legal judgment prediction methods usually only consider one single case fact description as input, which may not fully utilize the information in the data such as case relations and frequency. In this paper, we propose a new perspective that introduces some contrastive case relations to construct case triples as input, and a corresponding judgment prediction framework with case triples modeling (CTM). Our CTM can more effectively utilize beneficial information to refine the encoding and decoding processes through three customized modules, including the case triple module, the relational attention module, and the category decoder module. Finally, we conduct extensive experiments on two public datasets to verify the effectiveness of our CTM, including overall evaluation, compatibility analysis, ablation studies, analysis of gain source and visualization of case representations.
Anthology ID:
2022.coling-1.235
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2658–2667
Language:
URL:
https://aclanthology.org/2022.coling-1.235
DOI:
Bibkey:
Cite (ACL):
Dugang Liu, Weihao Du, Lei Li, Weike Pan, and Zhong Ming. 2022. Augmenting Legal Judgment Prediction with Contrastive Case Relations. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2658–2667, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Augmenting Legal Judgment Prediction with Contrastive Case Relations (Liu et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.235.pdf
Code
 dgliu/coling22_ctm