Multi-task Legal Judgement Prediction Combining a Subtask of Seriousness of Charge (2020)

Abstract: Legal Judgement Prediction has attracted more and more attention in recent years. One of the challenges is how to design a model with better interpretable prediction results. Previous studies have proposed different interpretable models based on the generation of court views and the extraction of charge keywords. Different from previous work, we propose a multi-task legal judgement prediction model which combines a subtask of the seriousness of charges. By introducing this subtask, our model can capture the attention weights of different terms of penalty corresponding to the charges and give more attention to the correct terms of penalty in the fact descriptions. Meanwhile, our model also incorporates the position of defendant making it capable of giving attention to the contextual information of the defendant. We carry several experiments on the public CAIL2018 dataset. Experimental results show that our model achieves better or comparable performance on three subtasks compared with the baseline models. Moreover, we also analyze the interpretable contribution of our model.

## introduction legal judgement prediction (ljp) aims to predict charge, law article and terms of penalty automatically based on the fact descriptions of the criminal cases. it can be used to help the court's judgement and provide legal guidance and assistance to the public. in recent years, different methods have been proposed to improve the performance of legal judgement prediction task. some previous studies need to design features manually @xcite @xcite and some of neural network based models extract features automatically and achieve significant improvements @xcite @xcite . however, there are still some challenging problems, including the improvement of the performance and the enhancement of the interpretability of the terms of penalty prediction. for the improvement of the performance in terms of penalty prediction, previous studies use multitask and joint learning to obtain the sharing information among different subtasks. @xcite propose a directed acyclic graph structure with topological relations to capture the information attribution among three subtasks, which effectively improve the problem of insufficient fine-grained in ljp. for the enhancement of the interpretability, different solutions are proposed to the problem. @xcite propose a seq2seq model to formulate legal judgement prediction task as a natural language generation problem. their model take fact descriptions and charge labels as input and outputs the court's view. the outputs are used as an auxiliary information for practical judgement. @xcite propose a multitask learning model to incorporate charge keywords extracted by tf-idf and textrank. their model has a good interpretability by introduced the keyword information. although different methods are proposed for the above two problems, we argue that some of the knowledge are known in legal judgement prediction task and can be incorporated into the model for improving the performance and the interpretability of the prediction results. figure 1 : attentions of different terms of penalty for charge of "murder" generated by the proposed subtask. as can be seen, term of death or life imprisonment and terms of 7-10 years are paid more attention for serious murder and less serious murder respectively. the first one is the seriousness of charges. actual judgement procedure tells us that the final decision of the terms of penalty is largely determined by the seriousness of the case, which depends on the case fact descriptions and the terms of penalty definition described in the article corresponding to the charge of the case. inspired by the actual judgement procedure, we propose to design a subtask of the seriousness of charges which is determined by the charge and the terms of penalty for the task of legal judgement prediction. according to the scope of legal terms of penalty, we can easily divide a fact description into two categories: serious and less serious. detailed descriptions and examples are given in section 3.3. the new subtask is used to obtain attentions of different terms of penalty according to serious and less serious predicted by the subtask and let the model pay more attention to those important terms of penalty for the corresponding fact descriptions. as an example with predicted charge as "murder" which is shown in fig. 1 ., we can see that our model captures more attention on "death or life imprisonment" with predicted serious label and on "7-10 years" with predicted less serious label, which is useful for the model selecting the right terms of penalty. the second one is the defendant information which is known in the case fact descriptions. previous studies focuses on the fact descriptions only (eg., just using text words), ignoring the importance of the context information of the defendant. to this end, we propose to incorporate the position of the defendant into the model. by introducing the defendant position-aware embedding for the fact descriptions, we can capture more context information of the defendant which is helpful for the prediction of subtasks. the main contributions of our work are as follows: ## related work legal judgement prediction task usually includes three subtasks: charges prediction, law articles recommendation and terms of penalty prediction. we will review the work of legal judgement prediction from single-task based models and multi-task based models. ## conclusion in this paper, we propose to design and combine a subtask of the seriousness of charges for multi-task legal judgement prediction. evaluations demonstrate the effectiveness of our model on charge prediction, law article recommendation and the terms of penalty prediction, indicating that the introduced subtask of the seriousness of charges and the sufficient encoding of the fact descriptions for the defendant are useful. our model also shows the good interpretability on the task of terms of penalty prediction. in c c l 2 0 2 0 the future, we will explore a better method to incorporate the contextual information of defendant and investigate the usefulness of different subtasks for multi-task legal judgement prediction.