Interpretable Charge Prediction for Criminal Cases with Dynamic Rationale Attention

Charge prediction which aims to determine appropriate charges for criminal cases based on textual fact descriptions, is an important technology in the field of AI&Law. Previous works focus on improving prediction accuracy, ignoring the interpretability, which limits the methods' applicability. In this work, we propose a deep neural framework to extract short but charge-decisive text snippets - rationales - from input fact description, as the interpretation of charge prediction. To solve the scarcity problem of rationale annotated corpus, rationales are extracted in a reinforcement style with the only supervision in the form of charge labels. We further propose a dynamic rationale attention mechanism to better utilize the information in extracted rationales and predict the charges. Experimental results show that besides providing charge prediction interpretation, our approach can also capture subtle details to help charge prediction.