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Publication . Conference object . Preprint . Article . 2018

FEVER: a Large-scale Dataset for Fact Extraction and VERification

James Thorne; Andreas Vlachos; Christos Christodoulopoulos; Arpit Mittal;
Open Access
Published: 14 Mar 2018
Publisher: Association for Computational Linguistics
Country: United Kingdom
Abstract
In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. It consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo by annotators achieving 0.6841 in Fleiss $\kappa$. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment. To characterize the challenge of the dataset presented, we develop a pipeline approach and compare it to suitably designed oracles. The best accuracy we achieve on labeling a claim accompanied by the correct evidence is 31.87%, while if we ignore the evidence we achieve 50.91%. Thus we believe that FEVER is a challenging testbed that will help stimulate progress on claim verification against textual sources.
Comment: Updated version of NAACL2018 paper. Data is released on http://fever.ai
Subjects by Vocabulary

Microsoft Academic Graph classification: Natural language processing computer.software_genre computer Scale (ratio) Computer science Pipeline (software) Fact extraction Artificial intelligence business.industry business Sentence

Subjects

Computer Science - Computation and Language, Computation and Language (cs.CL), FOS: Computer and information sciences

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Funded by
EC| SUMMA
Project
SUMMA
Scalable Understanding of Multilingual Media
  • Funder: European Commission (EC)
  • Project Code: 688139
  • Funding stream: H2020 | RIA
Related to Research communities
Digital Humanities and Cultural Heritage
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