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    <abstract>Legal Judgment Prediction (LJP) has attracted significant attention in recent years. However, previous studies have primarily focused on cases involving only a single defendant, skipping multi-defendant cases due to complexity and difficulty. To advance research, we introduce CMDL, a large-scale real-world Chinese Multi-Defendant LJP dataset, which consists of over 393,945 cases with nearly 1.2 million defendants in total. For performance evaluation, we propose case-level evaluation metrics dedicated for the multi-defendant scenario. Experimental results on CMDL show existing SOTA approaches demonstrate weakness when applied to cases involving multiple defendants. We highlight several challenges that require attention and resolution.</abstract>
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%0 Conference Proceedings
%T CMDL: A Large-Scale Chinese Multi-Defendant Legal Judgment Prediction Dataset
%A Huang, Wanhong
%A Feng, Yi
%A Li, Chuanyi
%A Wu, Honghan
%A Ge, Jidong
%A Ng, Vincent
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F huang-etal-2024-cmdl
%X Legal Judgment Prediction (LJP) has attracted significant attention in recent years. However, previous studies have primarily focused on cases involving only a single defendant, skipping multi-defendant cases due to complexity and difficulty. To advance research, we introduce CMDL, a large-scale real-world Chinese Multi-Defendant LJP dataset, which consists of over 393,945 cases with nearly 1.2 million defendants in total. For performance evaluation, we propose case-level evaluation metrics dedicated for the multi-defendant scenario. Experimental results on CMDL show existing SOTA approaches demonstrate weakness when applied to cases involving multiple defendants. We highlight several challenges that require attention and resolution.
%U https://aclanthology.org/2024.findings-acl.351
%P 5895-5906

Markdown (Informal)

[CMDL: A Large-Scale Chinese Multi-Defendant Legal Judgment Prediction Dataset](https://aclanthology.org/2024.findings-acl.351) (Huang et al., Findings 2024)

• CMDL: A Large-Scale Chinese Multi-Defendant Legal Judgment Prediction Dataset (Huang et al., Findings 2024)

ACL

• Wanhong Huang, Yi Feng, Chuanyi Li, Honghan Wu, Jidong Ge, and Vincent Ng. 2024. CMDL: A Large-Scale Chinese Multi-Defendant Legal Judgment Prediction Dataset. In Findings of the Association for Computational Linguistics ACL 2024, pages 5895–5906, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.