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        <title>A Fact Checking and Verification System for FEVEROUS Using a Zero-Shot Learning Approach</title>
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    <name type="personal">
        <namePart type="given">Orkun</namePart>
        <namePart type="family">Temiz</namePart>
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    <name type="personal">
        <namePart type="given">Özgün</namePart>
        <namePart type="given">Ozan</namePart>
        <namePart type="family">Kılıç</namePart>
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    <name type="personal">
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        <namePart type="family">Kızıldağ</namePart>
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    <name type="personal">
        <namePart type="given">Tuğba</namePart>
        <namePart type="family">Taşkaya Temizel</namePart>
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            <title>Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)</title>
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            <publisher>Association for Computational Linguistics</publisher>
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                <placeTerm type="text">Dominican Republic</placeTerm>
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    <abstract>In this paper, we propose a novel fact checking and verification system to check claims against Wikipedia content. Our system retrieves relevant Wikipedia pages using Anserini, uses BERT-large-cased question answering model to select correct evidence, and verifies claims using XLNET natural language inference model by comparing it with the evidence. Table cell evidence is obtained through looking for entity-matching cell values and TAPAS table question answering model. The pipeline utilizes zero-shot capabilities of existing models and all the models used in the pipeline requires no additional training. Our system got a FEVEROUS score of 0.06 and a label accuracy of 0.39 in FEVEROUS challenge.</abstract>
    <identifier type="citekey">temiz-etal-2021-fact</identifier>
    <identifier type="doi">10.18653/v1/2021.fever-1.13</identifier>
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        <url>https://aclanthology.org/2021.fever-1.13</url>
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        <date>2021-11</date>
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            <start>113</start>
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%0 Conference Proceedings
%T A Fact Checking and Verification System for FEVEROUS Using a Zero-Shot Learning Approach
%A Temiz, Orkun
%A Kılıç, Özgün Ozan
%A Kızıldağ, Arif Ozan
%A Taşkaya Temizel, Tuğba
%S Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Dominican Republic
%F temiz-etal-2021-fact
%X In this paper, we propose a novel fact checking and verification system to check claims against Wikipedia content. Our system retrieves relevant Wikipedia pages using Anserini, uses BERT-large-cased question answering model to select correct evidence, and verifies claims using XLNET natural language inference model by comparing it with the evidence. Table cell evidence is obtained through looking for entity-matching cell values and TAPAS table question answering model. The pipeline utilizes zero-shot capabilities of existing models and all the models used in the pipeline requires no additional training. Our system got a FEVEROUS score of 0.06 and a label accuracy of 0.39 in FEVEROUS challenge.
%R 10.18653/v1/2021.fever-1.13
%U https://aclanthology.org/2021.fever-1.13
%U https://doi.org/10.18653/v1/2021.fever-1.13
%P 113-120

Markdown (Informal)

[A Fact Checking and Verification System for FEVEROUS Using a Zero-Shot Learning Approach](https://aclanthology.org/2021.fever-1.13) (Temiz et al., FEVER 2021)

• A Fact Checking and Verification System for FEVEROUS Using a Zero-Shot Learning Approach (Temiz et al., FEVER 2021)

ACL

• Orkun Temiz, Özgün Ozan Kılıç, Arif Ozan Kızıldağ, and Tuğba Taşkaya Temizel. 2021. A Fact Checking and Verification System for FEVEROUS Using a Zero-Shot Learning Approach. In Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER), pages 113–120, Dominican Republic. Association for Computational Linguistics.