AI-lead Court Debate Case Investigation
This work addresses the need for efficient question generation in judicial trials to aid judges, but it is incremental as it builds on existing natural language generation methods for a specific domain.
The authors tackled the problem of generating questions for judges in multi-role court debates by proposing the Trial Brain Model (TBM), which learns questioning intentions from predefined knowledge and historical dialogue, resulting in more accurate questions as shown in experiments on real-world datasets.
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.