Leniency to those who confess?: Predicting the Legal Judgement via Multi-Modal Analysis

The Legal Judgement Prediction (LJP) is now under the spotlight. And it usually consists of multiple sub-tasks, such as penalty prediction (fine and imprisonment) and the prediction of articles of law. For penalty prediction, they are often closely related to the trial process, especially the attitude analysis of criminal suspects, which will influence the judgment of the presiding judge to some extent. In this paper, we firstly construct a multi-modal dataset with 517 cases of intentional assault, which contains trial information as well as the attitude of the suspect. Then, we explore the relationship between suspect`s attitude and term of imprisonment. Finally, we use the proposed multi-modal model to predict the suspect's attitude, and compare it with several strong baselines. Our experimental results show that the attitude of the criminal suspect is closely related to the penalty prediction, which provides a new perspective for LJP.