Through the MUD: A Multi-Defendant Charge Prediction Benchmark with Linked Crime Elements

Xiao Wei, Qi Xu, Hang Yu, Qian Liu, Erik Cambria

Abstract
The current charge prediction datasets mostly focus on single-defendant criminal cases.However, real-world criminal cases usually involve multiple defendants whose criminal facts are intertwined. In an early attempt to fill this gap, we introduce a new benchmark that encompasses legal cases involving multiple defendants, where each defendant is labeled with a charge and four types of crime elements, i.e., Object Element, Objective Element, Subject Element, and Subjective Element. Based on the dataset, we further develop an interpretable model called EJudge that incorporates crime elements and legal rules to infer charges. We observe that predicting crime charges while providing corresponding rationales benefits the interpretable AI system. Extensive experiments show that EJudge significantly surpasses state-of-the-art methods, which verify the importance of crime elements and legal rules in multi-defendant charge prediction. The source code and dataset are available at https://anonymous.4open.science/r/MCP_1-6010.
Anthology ID:
2024.acl-long.158
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2864–2878
Language:
URL:
https://aclanthology.org/2024.acl-long.158
DOI:
10.18653/v1/2024.acl-long.158
Bibkey:
Cite (ACL):
Xiao Wei, Qi Xu, Hang Yu, Qian Liu, and Erik Cambria. 2024. Through the MUD: A Multi-Defendant Charge Prediction Benchmark with Linked Crime Elements. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2864–2878, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Through the MUD: A Multi-Defendant Charge Prediction Benchmark with Linked Crime Elements (Wei et al., ACL 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-5/2024.acl-long.158.pdf
PDF Search