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        <title>Improving Evidence Retrieval with Claim-Evidence Entailment</title>
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    <name type="personal">
        <namePart type="given">Fan</namePart>
        <namePart type="family">Yang</namePart>
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        <namePart type="given">Eduard</namePart>
        <namePart type="family">Dragut</namePart>
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    <name type="personal">
        <namePart type="given">Arjun</namePart>
        <namePart type="family">Mukherjee</namePart>
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            <namePart type="given">Galia</namePart>
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    <abstract>Claim verification is challenging because it requires first to find textual evidence and then apply claim-evidence entailment to verify a claim. Previous works evaluate the entailment step based on the retrieved evidence, whereas we hypothesize that the entailment prediction can provide useful signals for evidence retrieval, in the sense that if a sentence supports or refutes a claim, the sentence must be relevant. We propose a novel model that uses the entailment score to express the relevancy. Our experiments verify that leveraging entailment prediction improves ranking multiple pieces of evidence.</abstract>
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        <url>https://aclanthology.org/2021.ranlp-1.174</url>
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        <date>2021-09</date>
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            <start>1553</start>
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%0 Conference Proceedings
%T Improving Evidence Retrieval with Claim-Evidence Entailment
%A Yang, Fan
%A Dragut, Eduard
%A Mukherjee, Arjun
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F yang-etal-2021-improving-evidence
%X Claim verification is challenging because it requires first to find textual evidence and then apply claim-evidence entailment to verify a claim. Previous works evaluate the entailment step based on the retrieved evidence, whereas we hypothesize that the entailment prediction can provide useful signals for evidence retrieval, in the sense that if a sentence supports or refutes a claim, the sentence must be relevant. We propose a novel model that uses the entailment score to express the relevancy. Our experiments verify that leveraging entailment prediction improves ranking multiple pieces of evidence.
%U https://aclanthology.org/2021.ranlp-1.174
%P 1553-1558

Markdown (Informal)

[Improving Evidence Retrieval with Claim-Evidence Entailment](https://aclanthology.org/2021.ranlp-1.174) (Yang et al., RANLP 2021)

• Improving Evidence Retrieval with Claim-Evidence Entailment (Yang et al., RANLP 2021)

ACL

• Fan Yang, Eduard Dragut, and Arjun Mukherjee. 2021. Improving Evidence Retrieval with Claim-Evidence Entailment. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1553–1558, Held Online. INCOMA Ltd..