2020
EMNLP
EMNLP 2020
Constrained Fact Verification for FEVER
Abstract
AbstractFact-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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Interdisciplinary Bridge
— Artificial Intelligence and Knowledge & Reasoning and Natural Language Processing
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Keyword Pioneer
— evidence reasoning
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Hot Topic Early Bird
— fact verification
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio