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Constrained Fact Verification for FEVER

Fact-verification systems are well explored in the NLP literature with growing attention owing to shared tasks like FEVER. Though the task requires reasoning on extracted evidence to verify a claim{'}s factuality, there is little work on understanding the reasoning process. In this work, we propose a new methodology for fact-verification, specifically FEVER, that enforces a closed-world reliance on extracted evidence. We present an extensive evaluation of state-of-the-art verification models under these constraints.

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