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Computer Science > Computation and Language

arXiv:2409.07055 (cs)
[Submitted on 11 Sep 2024]

Title:Legal Fact Prediction: Task Definition and Dataset Construction

Authors: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.
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
Cite as: arXiv:2409.07055 [cs.CL]
  (or arXiv:2409.07055v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2409.07055

Submission history

From: Shuyuan Zheng [view email]
[v1] Wed, 11 Sep 2024 07:01:08 UTC (1,377 KB)
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