Legal Judgment Prediction based on Knowledge-enhanced Multi-Task and Multi-Label Text Classification (2025)

Abstract: Legal judgment prediction (LJP) is an essential task for legal AI, aiming at predicting judgments based on the facts of a case. Legal judgments can involve multiple law articles and charges. Although recent methods in LJP have made notable progress, most are constrained to single-task settings (e.g., only predicting charges) or single-label settings (e.g., not accommodating cases with multiple charges), diverging from the complexities of real-world scenarios. In this paper, we address the challenge of predicting relevant law articles and charges within the framework of legal judgment prediction, treating it as a multi-task and multi-label text classification problem. We introduce a knowledge-enhanced approach, called K-LJP, that incorporates (I) ”label-level knowledge” (such as definitions and relationships among labels) to enhance the representation of case facts for each task, and (ii) ”task-level knowledge” (such as the alignment between law articles and corresponding charges) to improve task synergy. Comprehensive experiments demonstrate our method’s effectiveness in comparison to state-of-the-art (SOTA) baselines.

## introduction due to the recent advancement of machine learning, various nlp techniques have been applied in legal artificial intelligence (legalai) to assist judges in different ways, including controversy focus mining @xcite , legal document generation @xcite and legal judgment prediction @xcite . as one of the most important tasks in legalai, legal judgment prediction (ljp) has been studied for quite a long time @xcite . given the case's fact description, ljp aims to predict the likely judgment that includes law articles and charges. in the actual trial scene, as portrayed in fig. 1 , a case may involve several law articles and charges. however, most existing methods simplify the ljp to a single-label setting @xcite ) (e.g., ignoring multi-label cases) or single-task setting @xcite (e.g., predicting charges only), which is inconsistent with real-world scenarios. to bridge the gap between the simplified settings and the real scenarios, we formulate the problem of predicting law articles and charges as a multitask and multi-label text classification task and we utilize the corresponding legal knowledge. based on this we consider two levels of legal knowledge: 1) "label-level knowledge" in each task: all law articles and charges are defined with detailed definitions, and there are complex relationships among them (e.g., competing law articles). 2) "tasklevel knowledge" across tasks: as fig. 1 shows, law articles and charges are aligned in a many-tomany way. the alignment between law articles and charges can be represented as a mapping dictionary. however, the alignment is not strict (e.g., the multiple mapping charges of a law article are not all guaranteed to appear), which makes the two classification tasks linked with each other, yet without a means to be combined into a single task. in this paper, we propose a novel knowledgeenhanced ljp method (k-ljp) to incorporate the above mentioned types of legal knowledge into the model. for the "label-level knowledge", in each task, label definitions are used to obtain the initial embedding of each label, and the transformer decoder is retrofitted to learn the complex relationships among the labels and between the label and the case's fact simultaneously. for the "task-level knowledge", we design a special alignment loss that utilizes the alignment between the law articles and charges to synergize the tasks, and we construct two mapping dictionaries from the chinese code of law for the alignment loss calculation. ## problem formulation we first provide the definitions as follows. fact description consists of several sentences, which describe the events relevant to a case. here, we denote the fact description as @xmath0, where l f is the number of words in a fact description. judgment includes several law articles, and several charges. the law articles and the charges are in the form of labels. we denote the law articles as @xmath1, where l a and l c are the number of law articles and charges, respectively. 2 thus, the problem in our work is defined as: problem 1 (legal judgment prediction). given the fact description f , the task is to predict the judgment (a, c). ## method in this section, we describe the k-ljp method in detail. we begin by designing a vanilla model for the multi-task and multi-label text classification, and then introduce the proposed method which is an extended version of the vanilla model. ## conclusion and future work in this paper, we investigate the prediction of law articles and charges in the legal judgment prediction (ljp) task posited as a multi-task and multi-label problem. by incorporating legal knowledge, we propose a novel k-ljp approach. specifically, we inject "label-level knowledge" into the encoder by utilizing the label definitions and learning the complex relationships among the labels and between the label and the case's fact. then, since an alignment exists between law articles and charges, we design an alignment loss to integrate "task-level knowledge" into the model. experiments on a realworld dataset show the effectiveness of k-ljp. in the future, we will explore the following directions: 1) using similar cases to help the prediction. 2) predicting the term of penalty based on the predicted results of the law articles and charges.