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        <title>Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking</title>
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        <namePart type="given">Giannis</namePart>
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        <namePart type="given">Christina</namePart>
        <namePart type="family">Papagiannopoulou</namePart>
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    <name type="personal">
        <namePart type="given">Nikos</namePart>
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            <title>Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda</title>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Feldman</namePart>
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        <name type="personal">
            <namePart type="given">Giovanni</namePart>
            <namePart type="family">Da San Martino</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Chris</namePart>
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        <name type="personal">
            <namePart type="given">Preslav</namePart>
            <namePart type="family">Nakov</namePart>
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    <abstract>Fact Extraction and VERification (FEVER) is a recently introduced task that consists of the following subtasks (i) document retrieval, (ii) sentence retrieval, and (iii) claim verification. In this work, we focus on the subtask of sentence retrieval. Specifically, we propose an evidence-aware transformer-based model that outperforms all other models in terms of FEVER score by using a subset of training instances. In addition, we conduct a large experimental study to get a better understanding of the problem, while we summarize our findings by presenting future research challenges.</abstract>
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    <identifier type="doi">10.18653/v1/2021.nlp4if-1.4</identifier>
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        <date>2021-06</date>
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            <start>23</start>
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%0 Conference Proceedings
%T Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking
%A Bekoulis, Giannis
%A Papagiannopoulou, Christina
%A Deligiannis, Nikos
%Y Feldman, Anna
%Y Da San Martino, Giovanni
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F bekoulis-etal-2021-understanding
%X Fact Extraction and VERification (FEVER) is a recently introduced task that consists of the following subtasks (i) document retrieval, (ii) sentence retrieval, and (iii) claim verification. In this work, we focus on the subtask of sentence retrieval. Specifically, we propose an evidence-aware transformer-based model that outperforms all other models in terms of FEVER score by using a subset of training instances. In addition, we conduct a large experimental study to get a better understanding of the problem, while we summarize our findings by presenting future research challenges.
%R 10.18653/v1/2021.nlp4if-1.4
%U https://aclanthology.org/2021.nlp4if-1.4
%U https://doi.org/10.18653/v1/2021.nlp4if-1.4
%P 23-28

Markdown (Informal)

[Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking](https://aclanthology.org/2021.nlp4if-1.4) (Bekoulis et al., NLP4IF 2021)

• Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking (Bekoulis et al., NLP4IF 2021)

ACL

• Giannis Bekoulis, Christina Papagiannopoulou, and Nikos Deligiannis. 2021. Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking. In Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 23–28, Online. Association for Computational Linguistics.