The Sentencing-Element-Aware Model for Explainable Term-of-Penalty Prediction

Automatic term-of-penalty prediction is a key subtask of intelligent legal judgment (ILJ). Recent ILJ systems are based on deep learning methods, in which explainability is a pressing concern. In this paper, our goal is to build a term-of-penalty prediction system with good judicial explainability and high accuracy following the legal principles. We propose a sentencing-element-aware neural model to realize this. We introduce sentencing elements to link the case facts with legal laws, which makes the prediction meet the legal objectivity principle and ensure the accuracy. Meanwhile, in order to explain why the term-of-penalties are given, we output sentencing element-level explanations, and utilize sentencing elements to select the most similar cases as case-level explanations, which reflects the equity principle. Experiments on the datasets (CAIL2018) show that our model not only achieves equal or better accuracy than the baselines, but also provide useful explanations to help users to understand how the system works.