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.

提出了多被告行为描述的刑事判决预测任务，引入了分层推理链以及一种名为 HRN 的多被告刑事判决预测方法，同时构建了一个用于加速相关研究的真实世界多被告刑事判决预测数据集 MultiLJP。通过对 MultiLJP 的广泛实验验证了所提出的 HRN 方法的有效性。