Legal Fact Prediction: Task Definition and Dataset Construction

Junkai Liu, Yujie Tong, Hui Huang, Shuyuan Zheng, Muyun Yang, Peicheng Wu, Makoto Onizuka, Chuan Xiao
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Abstract

Legal facts refer to the facts that can be proven by acknowledged evidence in a trial. They form the basis for the determination of court judgments. This paper introduces a novel NLP task: legal fact prediction, which aims to predict the legal fact based on a list of evidence. The predicted facts can instruct the parties and their lawyers involved in a trial to strengthen their submissions and optimize their strategies during the trial. Moreover, since real legal facts are difficult to obtain before the final judgment, the predicted facts also serve as an important basis for legal judgment prediction. We construct a benchmark dataset consisting of evidence lists and ground-truth legal facts for real civil loan cases, LFPLoan. Our experiments on this dataset show that this task is non-trivial and requires further considerable research efforts.
法律事实预测:任务定义和数据集构建
法律事实是指可以通过公认的证据证明的事实。它们是法院判决的依据。本文介绍了一种新颖的 NLP 任务:法律事实预测,旨在根据证据清单预测法律事实。预测出的事实可以指导参与庭审的当事人及其律师在庭审过程中加强陈述并优化策略。此外,真实的法律事实在最终判决前很难获得,因此预测的事实也是法律判决预测的重要依据。我们构建了一个基准数据集 LFPLoan,该数据集由真实民间借贷案件的证据清单和地面真实法律事实组成。我们在该数据集上的实验表明,这项任务并不简单,需要进一步的大量研究工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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