CMDL: A Large-Scale Chinese Multi-Defendant Legal Judgment Prediction Dataset
Wanhong Huang, Yi Feng, Chuanyi Li, Honghan Wu, Jidong Ge, Vincent Ng
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.
BibTeX
@inproceedings{huang-etal-2024-cmdl,
title = "{CMDL}: A Large-Scale {C}hinese Multi-Defendant Legal Judgment Prediction Dataset",
author = "Huang, Wanhong and
Feng, Yi and
Li, Chuanyi and
Wu, Honghan and
Ge, Jidong and
Ng, Vincent",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.351/",
doi = "10.18653/v1/2024.findings-acl.351",
pages = "5895--5906"
}