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.

翻译：刑事案件事实描述中的多个被告通常表现出复杂的交互关系，现有法律判决预测（LJP）方法主要针对单被告案例预测判决结果（如法条、罪名和刑期），难以有效处理此类问题。为解决这一难题，我们提出多被告LJP任务，旨在自动预测多被告案件中每个被告的判决结果。该任务面临两大挑战：（1）不同被告间的判决结果难以区分；（2）缺乏用于训练和评估的真实世界数据集。针对第一个挑战，我们将多被告判决过程形式化为层次推理链，并提出一种名为层次推理网络（HRN）的多被告LJP方法，该方法遵循层次推理链为每个被告确定犯罪关系、量刑情节、法条、罪名和刑期。针对第二个挑战，我们收集了真实世界的多被告LJP数据集MultiLJP，以促进未来相关研究。在MultiLJP上的大量实验验证了所提出的HRN的有效性。