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        <title>QED: A fact verification system for the FEVER shared task</title>
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        <namePart type="given">Nanjiang</namePart>
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            <title>Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)</title>
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            <namePart type="given">James</namePart>
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    <abstract>This paper describes our system submission to the 2018 Fact Extraction and VERification (FEVER) shared task. The system uses a heuristics-based approach for evidence extraction and a modified version of the inference model by Parikh et al. (2016) for classification. Our process is broken down into three modules: potentially relevant documents are gathered based on key phrases in the claim, then any possible evidence sentences inside those documents are extracted, and finally our classifier discards any evidence deemed irrelevant and uses the remaining to classify the claim’s veracity. Our system beats the shared task baseline by 12% and is successful at finding correct evidence (evidence retrieval F1 of 62.5% on the development set).</abstract>
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%0 Conference Proceedings
%T QED: A fact verification system for the FEVER shared task
%A Luken, Jackson
%A Jiang, Nanjiang
%A de Marneffe, Marie-Catherine
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Christodoulopoulos, Christos
%Y Mittal, Arpit
%S Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F luken-etal-2018-qed
%X This paper describes our system submission to the 2018 Fact Extraction and VERification (FEVER) shared task. The system uses a heuristics-based approach for evidence extraction and a modified version of the inference model by Parikh et al. (2016) for classification. Our process is broken down into three modules: potentially relevant documents are gathered based on key phrases in the claim, then any possible evidence sentences inside those documents are extracted, and finally our classifier discards any evidence deemed irrelevant and uses the remaining to classify the claim’s veracity. Our system beats the shared task baseline by 12% and is successful at finding correct evidence (evidence retrieval F1 of 62.5% on the development set).
%R 10.18653/v1/W18-5526
%U https://aclanthology.org/W18-5526
%U https://doi.org/10.18653/v1/W18-5526
%P 156-160

Markdown (Informal)

[QED: A fact verification system for the FEVER shared task](https://aclanthology.org/W18-5526) (Luken et al., EMNLP 2018)

• QED: A fact verification system for the FEVER shared task (Luken et al., EMNLP 2018)

ACL

• Jackson Luken, Nanjiang Jiang, and Marie-Catherine de Marneffe. 2018. QED: A fact verification system for the FEVER shared task. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 156–160, Brussels, Belgium. Association for Computational Linguistics.