Case Retrieval for Legal Judgment Prediction in Legal Artificial Intelligence (2023)

Abstract: “Legal judgment prediction (LJP) is a basic task in legal artificial intelligence. It consists ofthree subtasks, which are relevant law article prediction, charge prediction and term of penaltyprediction, and gives the judgment results to assist the work of judges. In recent years, many deeplearning methods have emerged to improve the performance of the legal judgment prediction task. The previous methods mainly improve the performance by integrating law articles and the factdescription of a legal case. However, they rarely consider that the judges usually look up historicalcases before making a judgment in the actual scenario. To simulate this scenario, we propose ahistorical case retrieval framework for the legal judgment prediction task. Specifically, we selectsome historical cases which include all categories from the training dataset. Then, we retrieve themost similar Top-k historical cases of the current legal case and use the vector representation ofthese Top-k historical cases to help predict the judgment results. On two real-world legal datasets,our model achieves better results than several state-of-the-art baseline models.”

## introduction with the rapid development of artificial intelligence, it has become a trend to use artificial intelligence to help judicial personnel. legal judgment prediction (ljp) is such an artificial intelligence task in legal artificial intelligence. as shown in with the application of deep learning in legal artificial intelligence, various methods have been proposed to improve the performance of legal judgment prediction. some methods @xcite ) consider using the order information among the three subtasks of legal judgment prediction in reality to improve the representation of fact description. further, some methods @xcite @xcite consider a fine-grained division of the fact description to improve the fact representation. additionally, some methods @xcite @xcite consider the important role of law articles in reality and introduce them to improve the performance. these efforts have effectively improved the performance of legal judgment prediction. however, the existing methods are affected by the fact that the law articles are too concise and still have limitations in modelling the judgment process. on the one hand, the law articles are very concise and lack specific details and some law articles have similar provisions and so easy to be confused. as shown in figure 1 , article #114 and article #115 both stipulate the same charge crime of arson and the provisions in the two law articles are very short. in order to distinguish them, the judge usually re-finds and analyzes historical cases because the fact description information of historical cases usually contains more detailed information than the law articles. on the other hand, most of the previous methods predict the judgment results mainly based on the fact description of a single case, however, they overlook the practical scenario that judges usually look up typical historical cases for reference before making a judgment. as we all know, historical cases are very important for making a judgment, whether in the case law system or the statutory law system. 0 in the case law system, judges mainly refer to historical cases to make a judgment. in the statutory law system, before making a judgment the judges should not only look up the law articles but also look up typical historical cases. obviously, historical cases are indispensable references for judges in their work. to solve these challenges, we propose a framework for legal judgment prediction based on a historical case retrieval module to simulate the actual legal scenario of looking up historical cases before making a judgment. first, we consider that the number of cases looked up by judges in actual work is usually limited and select a part of cases from the training dataset as historical cases. second, in order to avoid the impact of highly unbalanced class distribution of the dataset on the model performance @xcite , we consider selecting the same number of historical cases for each category. third, we retrieve the most similar top-k historical cases of the current legal case and concatenate the vector representation of these top-k cases and the fact description of the current case to predict the judgment results. finally, we train our model with a cross-entropy loss function. we call our model cr4ljp, which stands for case retrieval framework for legal judgment prediction. our contributions are three-fold: ## problem definition before introducing our model, we first introduce some concepts and definitions of legal judgment prediction. a legal case in our paper consists of a fact description and three judgment results, which are made by human judges. the fact description is a text that describes the criminal facts of a suspect. as shown in figure 2 , our model uses f to represent it. the three judgment results are relevant law article, charge and term of penalty and we use y 1 , y 2 and y 3 to represent them respectively. then a legal case can be represented as: ## model framework in the actual judgment process, judges usually look up some typical historical cases for reference. to simulate this process, we propose a framework (cr4ljp) with a historical case retrieval module. ## conclusion in this paper, we first consider that judges usually look up some typical historical cases before making a judgment. we design a historical case retrieval model framework to simulate this scenario. for the current case, we retrieve the top-k similar historical cases and get vector representation of these cases using the basic encoder, then we concatenate the mean vector of them to the fact description vector to predict the judgment results. experimental results show that our method is effective.