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        <title>Automated Fact Checking: Task Formulations, Methods and Future Directions</title>
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            <namePart type="given">Emily</namePart>
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            <namePart type="family">Bender</namePart>
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    <abstract>The recently increased focus on misinformation has stimulated research in fact checking, the task of assessing the truthfulness of a claim. Research in automating this task has been conducted in a variety of disciplines including natural language processing, machine learning, knowledge representation, databases, and journalism. While there has been substantial progress, relevant papers and articles have been published in research communities that are often unaware of each other and use inconsistent terminology, thus impeding understanding and further progress. In this paper we survey automated fact checking research stemming from natural language processing and related disciplines, unifying the task formulations and methodologies across papers and authors. Furthermore, we highlight the use of evidence as an important distinguishing factor among them cutting across task formulations and methods. We conclude with proposing avenues for future NLP research on automated fact checking.</abstract>
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%0 Conference Proceedings
%T Automated Fact Checking: Task Formulations, Methods and Future Directions
%A Thorne, James
%A Vlachos, Andreas
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F thorne-vlachos-2018-automated
%X The recently increased focus on misinformation has stimulated research in fact checking, the task of assessing the truthfulness of a claim. Research in automating this task has been conducted in a variety of disciplines including natural language processing, machine learning, knowledge representation, databases, and journalism. While there has been substantial progress, relevant papers and articles have been published in research communities that are often unaware of each other and use inconsistent terminology, thus impeding understanding and further progress. In this paper we survey automated fact checking research stemming from natural language processing and related disciplines, unifying the task formulations and methodologies across papers and authors. Furthermore, we highlight the use of evidence as an important distinguishing factor among them cutting across task formulations and methods. We conclude with proposing avenues for future NLP research on automated fact checking.
%U https://aclanthology.org/C18-1283
%P 3346-3359

Markdown (Informal)

[Automated Fact Checking: Task Formulations, Methods and Future Directions](https://aclanthology.org/C18-1283) (Thorne & Vlachos, COLING 2018)

• Automated Fact Checking: Task Formulations, Methods and Future Directions (Thorne & Vlachos, COLING 2018)

ACL

• James Thorne and Andreas Vlachos. 2018. Automated Fact Checking: Task Formulations, Methods and Future Directions. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3346–3359, Santa Fe, New Mexico, USA. Association for Computational Linguistics.