# Legal Judgment Prediction via Heterogeneous Graphs and Knowledge of Law Articles
## 1 Introduction  
Legal intelligence aims to use artificial intelligence technologies, such as natural language processing and speech recognition, to empower the field of intelligent justice. Legal judgment prediction (LJP) is an essential task in the field of legal intelligence, especially for countries and regions that use civil law systems in which the judge decides the charge and the term of penalty based on the fact description of the case and the relevant law articles. The purpose of the LJP task is to predict the judgment of a case (charge, law article and term of penalty) based on the case fact description, which can help legal practitioners to judge the case and can also give professional legal advice to people who are not in the legal field. Currently, LJP tasks are generally regarded as text classification tasks [1]. Figure 1 shows the topology of LJP. Existing LJP methods have two main drawbacks. Firstly, the number of long texts in the LJP dataset is large, with texts having a length larger than 512 accounting for  
Figure 1: Legal judgment prediction. about 15% of the total and the longest text containing 56,694 words. Existing methods lack inference and representation for long documents and training data for LJP are fact description texts of cases. They focus on selecting deep-learning-based encoders, such as CNN-based encoders [2], RNN-based encoders and BERT-based encoders. The CNN-based encoder is not good at modeling and fully understanding sequential data such as text. Although an RNN-based encoder is good at modeling sequential data such as text, it has the natural disadvantage that too-long sequences can cause the model to lose information in backpropagation. Although BERT-based methods have made breakthroughs in different fields in recent years [3; 4; 5; 6; 7; 8], there is still a deficiency in terms of solving understanding long, domain-specific text, mainly because pre-trained models trained by generic corpora often do not work well in the legal field. There are currently several pre-trained models for the legal domain [9; 10; 11]. Nevertheless, there is only one open-source pre-training model for Chinese judicial scripts and its effect has been experimentally verified to have no significant performance improvement. Secondly, there is a confusing issue of charges (law articles), a current research hotspot in LJP. Existing work uses law articles to distinguish between similar charges, but a similar descriptive text can appear in the law articles corresponding to different charges. Therefore, it is still tricky to better use law articles to distinguish charges and existing work is still inadequate. Ref. [12] first used a traditional classifier to obtain the relevant law articles; then, they incorporated these relevant law articles in the model. However, this approach introduced too much noise and the results were not very satisfactory. The method proposed in [13] requires much expert knowledge and, in practice, we all prefer to use methods that can automatically obtain features to predict the judgment results. Ref. [14] used a graph neural network-based method to capture the differences in feature information between law articles, but it constructed text graphs in a relatively simple way that was not sufficient to adequately capture the different features of similar law articles. In order to solve the above problem, this paper proposes a new LJP method, which uses a graph neural network-based approach. Firstly, we use a graph network that extracts distinction of features between similar law articles for the law article graphs. Then, we construct a total of six kinds of different text graphs (heterogeneous graphs) for each fact description text and use a graph convolutional network (GCN) [15] to pass and update information inside the graph. A graph fusion method is used to integrate the node information of the six kinds of graph and the information of the law articles into the model. Finally, the fused vectors are used to predict the judgment results of the cases.