FEVER (Fact Extraction and VERification)

Introduced by Thorne et al. in FEVER: a large-scale dataset for Fact Extraction and VERification

FEVER is a publicly available dataset for fact extraction and verification against textual sources.

It consists of 185,445 claims manually verified against the introductory sections of Wikipedia pages and classified as SUPPORTED, REFUTED or NOTENOUGHINFO. For the first two classes, systems and annotators need to also return the combination of sentences forming the necessary evidence supporting or refuting the claim.

The claims were generated by human annotators extracting claims from Wikipedia and mutating them in a variety of ways, some of which were meaning-altering. The verification of each claim was conducted in a separate annotation process by annotators who were aware of the page but not the sentence from which original claim was extracted and thus in 31.75% of the claims more than one sentence was considered appropriate evidence. Claims require composition of evidence from multiple sentences in 16.82% of cases. Furthermore, in 12.15% of the claims, this evidence was taken from multiple pages.

Source: FEVER: a large-scale dataset for Fact Extraction and VERification


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