{"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","doi":"arxiv-2409.07055","DOIUrl":null,"url":null,"abstract":"Legal facts refer to the facts that can be proven by acknowledged evidence in\na trial. They form the basis for the determination of court judgments. This\npaper introduces a novel NLP task: legal fact prediction, which aims to predict\nthe legal fact based on a list of evidence. The predicted facts can instruct\nthe parties and their lawyers involved in a trial to strengthen their\nsubmissions and optimize their strategies during the trial. Moreover, since\nreal legal facts are difficult to obtain before the final judgment, the\npredicted facts also serve as an important basis for legal judgment prediction.\nWe construct a benchmark dataset consisting of evidence lists and ground-truth\nlegal facts for real civil loan cases, LFPLoan. Our experiments on this dataset\nshow that this task is non-trivial and requires further considerable research\nefforts.","PeriodicalId":501112,"journal":{"name":"arXiv - CS - Computers and Society","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Computers and Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.07055","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.