AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web

Michael Schlichtkrull · Zhijiang Guo · Andreas Vlachos

Great Hall & Hall B1+B2 (level 1) #334
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Thu 14 Dec 8:45 a.m. PST — 10:45 a.m. PST

Abstract: Existing datasets for automated fact-checking have substantial limitations, such as relying on artificial claims, lacking annotations for evidence and intermediate reasoning, or including evidence published after the claim. In this paper we introduce AVeriTeC, a new dataset of 4,568 real-world claims covering fact-checks by 50 different organizations. Each claim is annotated with question-answer pairs supported by evidence available online, as well as textual justifications explaining how the evidence combines to produce a verdict. Through a multi-round annotation process, we avoid common pitfalls including context dependence, evidence insufficiency, and temporal leakage, and reach a substantial inter-annotator agreement of $\kappa=0.619$ on verdicts. We develop a baseline as well as an evaluation scheme for verifying claims through question-answering against the open web.

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