Interpretable Charge Prediction for Legal Cases based on Interdependent Legal Information

The interpretable charge prediction task is to predict the final charges according to the fact descriptions, at the same time generate the corresponding explanations. The task is often more difficult than people think, as the explanation and prediction are highly interdependent, which is ignored by existing methods. In this paper, we deal with the interpretable charge prediction task from the perspective of generating the prediction and explanation (court view) pair while considering their dependency. To this end, we propose a Joint Prediction and Generation Model, named JPGM, which includes a coarse-to-fine classifier and a keyword-aware generator. Specifically, firstly, the classifier predicts a group of charge labels given the fact description. Then, we select the most matchable keywords of every charge from the pre-defined charge-discriminative keywords by an attention mechanism. Furthermore, the generator generates explanation using both the fact description and the most matchable keywords. Finally, to refine the prediction, the generated explanation and fact description are fused for final charge prediction. The classifier and generator are trained under an alternating procedure, which alleviates the error propagation. The experimental results validate that our model can effectively address the dependency issue and predict the charge with more interpretability.