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Enhancing information retrieval in fact extraction and verification

Guzman Olivares, Daniel, Quijano, Lara and Liberatore, Federico ORCID: 2023. Enhancing information retrieval in fact extraction and verification. Presented at: Sixth Fact Extraction and VERification Workshop (FEVER), Dubrovnik, Croatia, Proceedings of the Sixth Fact Extraction and VERification Workshop (FEVER). Association for Computational Linguistics, pp. 34-48.

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Modern fact verification systems have distanced themselves from the black box paradigm by providing the evidence used to infer their veracity judgments. Hence, evidence-backed fact verification systems’ performance heavily depends on the capabilities of their retrieval component to identify these facts. A popular evaluation benchmark for these systems is the FEVER task, which consists of determining the veracity of short claims using sentences extracted from Wikipedia. In this paper, we present a novel approach to the the retrieval steps of the FEVER task leveraging the graph structure of Wikipedia. The retrieval models surpass state of the art results at both sentence and document level. Additionally, we show that by feeding our retrieved evidence to the best-performing textual entailment model, we set a new state of the art in the FEVER competition.

Item Type: Conference or Workshop Item (Paper)
Status: Published
Schools: Computer Science & Informatics
Publisher: Association for Computational Linguistics
Date of First Compliant Deposit: 14 June 2023
Date of Acceptance: 13 March 2023
Last Modified: 29 Jun 2023 10:00

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