  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.
