Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning (2023)

Abstract: 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.

## introduction legal judgment prediction (ljp) aims at predicting judgment results (e.g., law articles, applicable charges, and terms of penalty) based on the fact description of a given case. existing ljp studies primarily focus on single-defendant cases, where only one defendant is involved @xcite @xcite @xcite @xcite @xcite . figure 1 : an illustration of multi-defendant ljp. generally, a judge needs to reason on the fact description to clarify the complex interactions among different defendants and make accurate judgments for each defendant. despite these successful efforts, singledefendant ljp suffers from an inevitable restriction in practice: a large number of fact descriptions with multiple defendants. according to statistics derived from published legal documents sampled from legal case information disclosure, multidefendant cases constitute a minimum of 30% of all cases @xcite . as shown in figure 1 , the multi-defendant ljp task aims at predicting law articles, charges, and terms of penalty for each defendant in multi-defendant cases. since multiple defendants are mentioned in the fact description and exhibit complex interactions, the multi-defendant ljp task requires clarifying these interactions and making accurate judgments for each defendant, which is intuitively beyond the reach of single-defendant ljp methods. hence, there is a pressing need to extend ljp from single-defendant to multi-defendant scenarios. however, two main challenges arise with the task of multi-defendant ljp: as shown in figure 1 , in order to distinguish different judgment results among various defendants, a judge has to clarify criminal relationships among defendants to determine whether defendants share same law articles and charges, and sentencing circumstances affecting terms of penalty for each defendant. based on these intermediate reasoning results, the judge determines and verifies the judgment results (law articles, charges, and terms of penalty) for each defendant, following a forward and backward order. the motivation behind forward prediction and backward verification is rooted in the complex nature of legal reasoning, where evidence and conclusions can be interdependent @xcite . overall, the multi-defendant judgment process requires simulating the judicial logic of human judges and modeling complex reasoning chains. existing datasets for ljp either only have single-defendant cases annotated for multiple ljp subtasks or multi-defendant cases for a single ljp subtask. xiao et al. (2018) collect a real-world ljp dataset cail, which only retains single-defendant cases. pan et al. (2019) only annotate multi-defendant cases with the charge prediction subtask and ignore information about criminal relationships and sentencing circumstances that can distinguish judgment results of multiple defendants in real scenarios. in order to accelerate the research on multi-defendant ljp, we urgently need a real-world multi-defendant ljp dataset. to tackle the first challenge, we formalize the multi-defendant judgment process as hierarchical reasoning chains and propose a method for multidefendant ljp, named hierarchical reasoning network (hrn), which follows the hierarchical reasoning chains to distinguish different judgment results of various defendants. specifically, the hi-erarchical reasoning chains are divided into two levels. the first-level reasoning chain identifies the relationships between defendants and determines the sentencing circumstances for each defendant. the second-level reasoning chain predicts and verifies the law articles, charges, and terms of penalty for each defendant, using a forward prediction process and a backward verification process, respectively. since generative language models have shown great ability to reason @xcite @xcite , we convert these reasoning chains into sequenceto-sequence (seq2seq) generation tasks and apply the mt5 @xcite to model them. furthermore, we adopt fusion-in-decoder (fid) @xcite to process multi-defendant fact descriptions with thousands of tokens efficiently. to tackle the second challenge, we collect a realworld dataset, namely multiljp, with 23,717 realworld multi-defendant ljp cases. eight professional annotators are involved in manually editing law articles, charges, terms of penalty, criminal relationships, and sentencing circumstances for each defendant. in 89.58 percent of these cases, the defendants have different judgment results for at least one of the subtasks of the multi-defendant ljp task. multiljp requires accurate distinction of the judgment results for each defendant. this makes multiljp different from the existing singledefendant ljp datasets. our work provides the first benchmark for the multi-defendant ljp task. using multiljp, we evaluate the effectiveness of hrn for multi-defendant ljp on various subtasks. the results show that hrn can significantly outperform all the baselines. in summary, our main contributions are: • we focus on the multi-defendant ljp task and formalize the multi-defendant judgment process as hierarchical reasoning chains for the multidefendant ljp task. ## dataset in this section, we describe the construction process and analyze various aspects of multiljp to provide a deeper understanding of the dataset. ## method in this section, we describe the hrn method. first, we formulate our research problem. then, we introduce the sequence-to-sequence (seq2seq) gen-eration framework for hierarchical reasoning. next, we introduce hierarchical reasoning chains of multidefendant in detail. finally, a training process with fusion-in-decoder for hrn is explained. ## experimental results and analysis in this section, we first conduct multi-defendant legal judgment predictions and ablation studies to answer the research questions listed in the section. 5.1. in addition, we also conducted a case study to intuitively evaluate the importance of hierarchical reasoning. ## conclusions in this paper, we studied the legal judgment prediction problem for multi-defendant cases. we proposed the task of multi-defendant ljp to promote ljp systems from single-defendant to multidefendant. to distinguish confusing judgment results of different defendants, we proposed a hierarchical reasoning network (hrn) to determine criminal relationships, sentencing circumstances, law articles, charges and terms of penalty for each defendant. as there is no benchmark dataset for multi-defendant ljp, we have collected a realworld dataset multiljp. we conducted extensive experiments on the multiljp dataset. experimental results have verified the effectiveness of our proposed method and hrn outperforms all baselines.