Charge Prediction with Legal Attention

Charge prediction aims to predict the corresponding charges for a specific case. In civil law system, human judges will match the facts with relevant laws, and the final judgments are usually made in accordance with relevant law articles. Existing works either ignore this feature or simply model the relationship using multi-task learning, but neither make full use of relevant articles to assist the charge prediction task. To address this issue, we propose an attentional neural network, LegalAtt, which uses relevant articles to improve the performance and interpretability of charge prediction task. More specifically, our model works in a bidirectional approach: First, it uses the fact description to extract relevant articles; In return, the selected relevant articles assist to locate key information from the fact description, which helps improve the performance of charge prediction. Experimental results show that our model achieves the best performance on the real-world dataset compared with other state-of-the-art baselines. Our code is available at https://github.com/nlp208/legal_attention.