Legal Fact Prediction: Task Definition and Dataset Construction
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 ca...
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creator | Liu, Junkai Tong, Yujie Huang, Hui Zheng, Shuyuan Yang, Muyun Wu, Peicheng Onizuka, Makoto Xiao, Chuan |
description | 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. |
doi_str_mv | 10.48550/arxiv.2409.07055 |
format | Article |
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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.</description><identifier>DOI: 10.48550/arxiv.2409.07055</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; Computer Science - Computers and Society</subject><creationdate>2024-09</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2409.07055$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2409.07055$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Junkai</creatorcontrib><creatorcontrib>Tong, Yujie</creatorcontrib><creatorcontrib>Huang, Hui</creatorcontrib><creatorcontrib>Zheng, Shuyuan</creatorcontrib><creatorcontrib>Yang, Muyun</creatorcontrib><creatorcontrib>Wu, Peicheng</creatorcontrib><creatorcontrib>Onizuka, Makoto</creatorcontrib><creatorcontrib>Xiao, Chuan</creatorcontrib><title>Legal Fact Prediction: Task Definition and Dataset Construction</title><description>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.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Computation and Language</subject><subject>Computer Science - Computers and Society</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNpjYJA0NNAzsTA1NdBPLKrILNMzMjGw1DMwNzA15WSw90lNT8xRcEtMLlEIKEpNyUwuyczPs1IISSzOVnBJTcvMywQJKCTmpSi4JJYkFqeWKDjn5xWXFJWCVfIwsKYl5hSn8kJpbgZ5N9cQZw9dsFXxBUWZuYlFlfEgK-PBVhoTVgEAe8Y2AQ</recordid><startdate>20240911</startdate><enddate>20240911</enddate><creator>Liu, Junkai</creator><creator>Tong, Yujie</creator><creator>Huang, Hui</creator><creator>Zheng, Shuyuan</creator><creator>Yang, Muyun</creator><creator>Wu, Peicheng</creator><creator>Onizuka, Makoto</creator><creator>Xiao, Chuan</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20240911</creationdate><title>Legal Fact Prediction: Task Definition and Dataset Construction</title><author>Liu, Junkai ; Tong, Yujie ; Huang, Hui ; Zheng, Shuyuan ; Yang, Muyun ; Wu, Peicheng ; Onizuka, Makoto ; Xiao, Chuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2409_070553</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Computation and Language</topic><topic>Computer Science - Computers and Society</topic><toplevel>online_resources</toplevel><creatorcontrib>Liu, Junkai</creatorcontrib><creatorcontrib>Tong, Yujie</creatorcontrib><creatorcontrib>Huang, Hui</creatorcontrib><creatorcontrib>Zheng, Shuyuan</creatorcontrib><creatorcontrib>Yang, Muyun</creatorcontrib><creatorcontrib>Wu, Peicheng</creatorcontrib><creatorcontrib>Onizuka, Makoto</creatorcontrib><creatorcontrib>Xiao, Chuan</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Liu, Junkai</au><au>Tong, Yujie</au><au>Huang, Hui</au><au>Zheng, Shuyuan</au><au>Yang, Muyun</au><au>Wu, Peicheng</au><au>Onizuka, Makoto</au><au>Xiao, Chuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Legal Fact Prediction: Task Definition and Dataset Construction</atitle><date>2024-09-11</date><risdate>2024</risdate><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.</abstract><doi>10.48550/arxiv.2409.07055</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Computers and Society |
title | Legal Fact Prediction: Task Definition and Dataset Construction |
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