Learning to Predict Charges for Judgment with Legal Graph

Abstract

The automatic charge prediction aims to predict the result of the judgment through fact descriptions in criminal cases, which is an important application of intelligent legal judgment system. Generally, this task can be formalized into a multi-label prediction task (i.e., we treat fact descriptions as inputs, and charges as labels). Most previous works on this task usually exploit informative features from fact descriptions for prediction while ignoring the charge space information (e.g., co-occurrence relation of charges or descriptions of charges). To better explore the charge space, in this paper, we propose to establish a Legal Graph Network (LGN for short) to solve this problem. Specifically, LGN fuses all the charge information (i.e., charge descriptions or correlations) into a unified legal graph. Based on the legal graph, four types of charge relations are designed to capture informative relations among charges. Then LGN embeds these relations to learn the robust charge representations. Finally both charge representations and fact representations are fed into an attention-based neural network for prediction. Experimental results on three datasets show that the model we proposed can significantly outperform state-of-the-art multi-label classification methods.