Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework (2022)

Abstract: Legal judgment prediction (LJP) is a fundamental task in legal AI, which aims to assist the judge to hear the case and determine the judgment. The legal judgment usually consists of the law article, charge, and term of penalty. In the real trial scenario, the judge usually makes the decision step-by-step: first concludes the rationale according to the case’s facts and then determines the judgment. Recently, many models have been proposed and made tremendous progress in LJP, but most of them adopt an end-to-end manner that cannot be manually intervened by the judge for practical use. Moreover, existing models lack interpretability due to the neglect of rationale in the prediction process. Following the judge’s real trial logic, in this paper, we propose a novel Rationale-based Legal Judgment Prediction (RLJP) framework. In the RLJP framework, the LJP process is split into two steps. In the first phase, the model generates the rationales according to the fact description. Then it predicts the judgment based on the fact and the generated rationales. Extensive experiments on a real-world dataset show RLJP achieves the best results compared to the state-of-the-art models. Meanwhile, the proposed framework provides good interactivity and interpretability which enables practical use.

## introduction deep learning methods, especially the natural language process (nlp) techniques, have been employed to benefit the legal assistant systems in several aspects, such as controversy focus mining @xcite and court hearing conversation generation @xcite . as one of the most important components of the judicial process, the task of legal judgment prediction (ljp) has been studied for decades @xcite @xcite @xcite . ljp is defined to predict the judgment results (e.g., law article, charge, and term of penalty) of legal cases according to the fact descriptions. in the real trial scenario, as fig. 1 shows, the judge makes the decision step-by-step: first concludes the rationale according to the case's facts and then determines the judgment. however, most existing methods focus on designing efficient features following an end-to-end framework @xcite while neglecting the necessity for a system to be interactive and interpretable in real trial scenarios. to improve the practicality and effectiveness of ljp methods in real world applications, we face the following challenges: 1) the prediction process cannot be manually intervened: legal tasks usually require high accuracy, but current ljp methods are commonly designed as an end-to-end manner in which the reasoning part is a "blackbox". in practical use, the judge is hard to intervene the trial logic during the prediction process, which may amplify the error and reduce the applicability of the method. 2) the predicted judgment lacks interpretability: the lack of interpretation is a common problem for deep learning models. however, it is especially important in the judicial field since every judgment should be given with a thorough explanation of reasoning to make the verdict more convincing. following the judge's trial logic, in this paper, we propose a novel rationale-based legal judgment prediction (rljp) framework to incorporate the rationale into the ljp task. in the rljp framework, the ljp process is divided into two steps. in the first step, it generates the charge rationale and penalty rationale respectively according to the fact description. next, it predicts the judgment based on the fact and the corresponding rationales generated in the first step. moreover, with the proposed topatt mechanism, our framework is able to better connect the two steps and also learn the topological dependencies among the three subtasks of judg- ## problem formulation in this work, we explore the problem of legal judgment prediction. we first clarify the definitions of the terms as follows. fact description consists of several descriptive sentences, which describes the identified facts (e.g., relevant events occurred in a case). here, we denote the fact description as @xmath0, where l f is the number of words in fact description. rationale is concluded from the fact description in order to determine and interpret the judgment. notably, there exists corresponding rationales for both charge and term of penalty, named as charge rationale and penalty rationale respectively. here, we denote the charge rationale as @xmath1, where l cr is the number of words in charge rationale and l pr is the number of words in penalty rationale. judgment includes the referred law articles, the charge and the term of penalty, which is determined by the judge according to the fact and rationales. here, we denote the referred law article as a, the charge as c, and the term of penalty as p. the law article and the charge are in the form of labels, and the term of penalty is represented in months (numerical values). in this work, we follow a "biomimic" design by simulating the thinking logic of judges when making decisions. we take the rationale into consideration, then the problem is defined as: problem 1 (legal judgment prediction). given the fact description f , our task is to generate the rationales (cr, pr) and predict the judgment (a, c, p). ## rationale-based legal judgment prediction (rljp) framework in this section, we describe our rationale-based legal judgment prediction (rljp) framework in detail. fig. 2 shows the overall infrastructure of the proposed framework. it consists of a rationale generation module and a judgment prediction module. the generation module takes the fact description as input; the prediction module takes the fact description and the output of the generation module as input. since the two modules work separately, when the generated rationales contain mistakes, manual intervention can be taken to make corrections before judgment prediction. ## ethical discussion while ai is gaining adoption in legal justice, ethical issues have also gained increasing attention since any subtle miscalculation may trigger serious consequences @xcite . in such circumstances, our framework is more appropriate to offer suggestions to the judges rather than making final judgment without humans intervention. indeed, our purpose is to provide assistance to the judges and improve their work efficiency. the system allows the judges to adjust the generated rationales and make modifications if needed. ## conclusion and future work in this paper, we propose a novel rationale-based legal judgment prediction (rljp) framework to solve the task of legal judgment prediction in criminal cases by thoroughly taking interactivity and interpretability into consideration. referring to the judge's practical trial logic, the rljp splits the classical end-to-end prediction into two phases: in the first stage, it generates the rationale according to the fact description. next, it predicts the judgment based on the fact and the generated rationale. experiments on a real-world dataset clearly show that the proposed framework provides good interactivity and interpretability which enables practical use, and also achieves better performance on the task of legal judgment prediction compared to the state-of-the-art models. based on the rljp framework, in the future, we can explore the following directions: (1) using the content of the law articles to improve the prediction of the term of penalty. (2) providing more references (e.g., similar cases) for the task of rationale generation.