AI-lead Court Debate Case Investigation

TL;DR

This paper introduces Trial Brain Model (TBM), an end-to-end question generation system that learns judicial questioning intent from dialogue history, improving question accuracy in multi-role court debates.

Contribution

The paper presents a novel TBM that incorporates predefined knowledge to generate more accurate judicial questions, advancing natural language generation in legal contexts.

Findings

TBM outperforms baseline models in question accuracy

Model effectively learns judge's questioning intent

Experiments on real-world datasets validate effectiveness

Abstract

The multi-role judicial debate composed of the plaintiff, defendant, and judge is an important part of the judicial trial. Different from other types of dialogue, questions are raised by the judge, The plaintiff, plaintiff's agent defendant, and defendant's agent would be to debating so that the trial can proceed in an orderly manner. Question generation is an important task in Natural Language Generation. In the judicial trial, it can help the judge raise efficient questions so that the judge has a clearer understanding of the case. In this work, we propose an innovative end-to-end question generation model-Trial Brain Model (TBM) to build a Trial Brain, it can generate the questions the judge wants to ask through the historical dialogue between the plaintiff and the defendant. Unlike prior efforts in natural language generation, our model can learn the judge's questioning intention through predefined knowledge. We do experiments on real-world datasets, the experimental results show that our model can provide a more accurate question in the multi-role court debate scene.