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We introduce a FEVER-like dataset COVID-Fact of $4,086$ claims concerning theCOVID-19 pandemic. The dataset contains claims, evidence for the claims, andcontradictory claims refuted by the evidence. Unlike previous approaches, weautomatically detect true claims and their source articles and then generatecounter-claims using automatic methods rather than employing human annotators.Along with our constructed resource, we formally present the task ofidentifying relevant evidence for the claims and verifying whether the evidencerefutes or supports a given claim. In addition to scientific claims, our datacontains simplified general claims from media sources, making it better suitedfor detecting general misinformation regarding COVID-19. Our experimentsindicate that COVID-Fact will provide a challenging testbed for the developmentof new systems and our approach will reduce the costs of buildingdomain-specific datasets for detecting misinformation.

COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic