  Legal judgment prediction(LJP) is an essential task for legal AI. While prior
methods studied on this topic in a pseudo setting by employing the
judge-summarized case narrative as the input to predict the judgment,
neglecting critical case life-cycle information in real court setting could
threaten the case logic representation quality and prediction correctness. In
this paper, we introduce a novel challenging dataset from real courtrooms to
predict the legal judgment in a reasonably encyclopedic manner by leveraging
the genuine input of the case -- plaintiff's claims and court debate data, from
which the case's facts are automatically recognized by comprehensively
understanding the multi-role dialogues of the court debate, and then learnt to
discriminate the claims so as to reach the final judgment through multi-task
learning. An extensive set of experiments with a large civil trial data set
shows that the proposed model can more accurately characterize the interactions
among claims, fact and debate for legal judgment prediction, achieving
significant improvements over strong state-of-the-art baselines. Moreover, the
user study conducted with real judges and law school students shows the neural
predictions can also be interpretable and easily observed, and thus enhancing
the trial efficiency and judgment quality.
