Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning
Yougang Lyu Jitai Hao Zihan Wang Kai Zhao Shen Gao Pengjie Ren Zhumin Chen Fang Wang Zhaochun Ren
Multiple defendants in a criminal fact description generally exhibit complex
interactions, and cannot be well handled by existing Legal Judgment Prediction
(LJP) methods which focus on predicting judgment results (e.g., law articles,
charges, and terms of penalty) for single-defendant cases. To address this
problem, we propose the task of multi-defendant LJP, which aims to
automatically predict the judgment results for each defendant of
multi-defendant cases. Two challenges arise with the task of multi-defendant
LJP: (1) indistinguishable judgment results among various defendants; and (2)
the lack of a real-world dataset for training and evaluation. To tackle the
first challenge, we formalize the multi-defendant judgment process as
hierarchical reasoning chains and introduce a multi-defendant LJP method, named
Hierarchical Reasoning Network (HRN), which follows the hierarchical reasoning
chains to determine criminal relationships, sentencing circumstances, law
articles, charges, and terms of penalty for each defendant. To tackle the
second challenge, we collect a real-world multi-defendant LJP dataset, namely
MultiLJP, to accelerate the relevant research in the future. Extensive
experiments on MultiLJP verify the effectiveness of our proposed HRN.