An Element-aware Multi-representation Model for Law Article Prediction (2020)

Abstract: Existing works have proved that using law articles as external knowledge can improve the performance of the Legal Judgment Prediction. However, they do not fully use law article information and most of the current work is only for single label samples. In this paper, we propose a Law Article Element-aware Multi-representation Model (LEMM), which can make full use of law article information and can be used for multi-label samples. The model uses the labeled elements of law articles to extract fact description features from multiple angles. It generates multiple representations of a fact for classification. Every label has a law-aware fact representation to encode more information. To capture the dependencies between law articles, the model also introduces a self-attention mechanism between multiple representations. Compared with baseline models like TopJudge, this model improves the accuracy of 5.84%, the macro F1 of 6.42%, and the micro F1 of 4.28%.

## introduction legal judgment prediction(ljp) aims to predict a law case's judgment results given a fact description text. ljp mainly contains three sub-tasks, law article prediction, charge prediction, and terms of penalty prediction. in the civil law system, the correct prediction of law article prediction can help improve the accuracy of charge prediction @xcite . the investigation of law article prediction has significant meaning for ljp. the law article prediction aims to predict the case's relevant law articles given the fact description (hereinafter abbreviated fact) of a case. in the law article prediction, law articles play an essential role as external information. @xcite uses some candidate law articles to improve the performance of the charge prediction task. however, current researches have two main limitations. one is that certain law articles are considerably similar which makes them difficult to distinguish. using the representation of overall law articles to extract fact information is not intuitive enough. another one is that most of the works @xcite @xcite only predict on single label examples. meanwhile, in the actual judgment, many cases contain multiple relevant law articles @xcite . human judge process mainly compares the elements of law article with the case description @xcite , such as the subject of crime (person or specific identity), the object of the crime (person or thing), the purpose and motive of the crime, the harmful behavior, the adverse result, and the crime scene (time or place). to make full use of the law article information and reduce the confusion in distinguishing different law articles, we have designed a law article element-aware multi-representation model (lemm). lemm is more related to human cognitive logic and more intuitive based on the law element. we call it lemm because it extracts fact features specifically by using law article elements and generates multiple law-aware fact representations. each label has a particular fact representation in classification, which benefits the law article prediction task. using one vector to distinguish correct law article is inappropriate because the number of relevant law articles is more than 100. considering law-aware fact representation takes law article as an individual unit, and there are some dependencies between law articles, we capture the relationship between them via the self-attention mechanism. our lemm model makes an excellent performance in all evaluation indicators. 2 structurally labeling the law articles judging whether the law article and case are relevant mainly depends on whether the key elements (including the subject, object, purpose, motive, the harmful behavior, the result of the harm, and the circumstances of the crime) are consistent with the law. therefore, we divide the law articles into seven elements: 1. the crime subject, 2. the crime object, 3. the purpose and motive of the crime, 4. the harmful behavior, 5. the harmful result, 6. the crime occasion and 7. the supplementary explanation. since a law article may contain multiple crimes, such law article has multiple groups of elements which correspond to different crimes. as shown in figure 1 , we first divide the content of the law according to the crime and then label the various elements. for elements that are not specified or restricted, we mark them as none. we label 183 candidate law articles of the cail dataset @xcite , which contains a total of 202 crimes. ## lemm model the fact is a word sequence {w 1 , w 2 , . . . , w m }. the model uses labeled law articles to help extract features of the fact. the labeled law articles contain the name of crime and the elements of the crime. the name of crime is a word sequence: {w 1 , w 2 , . . . ., w n }. the elements of crime contain seven word sequences: {ele 1 , ele 2 , . . . , ele 7 }, where ele i is {w 1 , w 2 , . . . , w ik }. our model contains five components: encoder: encode law article elements and fact. ## experiments this part includes data selection, experimental parameter setting, baseline model, and detailed experimental results. ## conclusion we propose a model that predicts relevant law articles on multi-label samples by simulating the human judging process. our proposed lemm model uses elements of the manually labeled law articles to generate multiple representations of a fact. it uses self-attention to capture dependencies between law articles and makes a unique representation for each candidate label for prediction. the experiments verify that the element-aware multirepresentation can better extract features of the factual information and the dependencies between law articles are beneficial to the law article prediction task. the model achieves state-of-the-art performance in benchmark datasets. it also fills the gap between experimental and practical applications on multi-label samples.