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Multiple defendants in a criminal fact description generally exhibit complexinteractions, 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 thisproblem, we propose the task of multi-defendant LJP, which aims toautomatically predict the judgment results for each defendant ofmulti-defendant cases. Two challenges arise with the task of multi-defendantLJP: (1) indistinguishable judgment results among various defendants; and (2)the lack of a real-world dataset for training and evaluation. To tackle thefirst challenge, we formalize the multi-defendant judgment process ashierarchical reasoning chains and introduce a multi-defendant LJP method, namedHierarchical Reasoning Network (HRN), which follows the hierarchical reasoningchains to determine criminal relationships, sentencing circumstances, lawarticles, charges, and terms of penalty for each defendant. To tackle thesecond challenge, we collect a real-world multi-defendant LJP dataset, namelyMultiLJP, to accelerate the relevant research in the future. Extensiveexperiments on MultiLJP verify the effectiveness of our proposed HRN.

Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning