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        <title>Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging</title>
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    <name type="personal">
        <namePart type="given">Nayeon</namePart>
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    <name type="personal">
        <namePart type="given">Chien-Sheng</namePart>
        <namePart type="family">Wu</namePart>
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    <name type="personal">
        <namePart type="given">Pascale</namePart>
        <namePart type="family">Fung</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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            <namePart type="given">David</namePart>
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            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Fact-checking of textual sources needs to effectively extract relevant information from large knowledge bases. In this paper, we extend an existing pipeline approach to better tackle this problem. We propose a neural ranker using a decomposable attention model that dynamically selects sentences to achieve promising improvement in evidence retrieval F1 by 38.80%, with (x65) speedup compared to a TF-IDF method. Moreover, we incorporate lexical tagging methods into our pipeline framework to simplify the tasks and render the model more generalizable. As a result, our framework achieves promising performance on a large-scale fact extraction and verification dataset with speedup.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1143</identifier>
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        <date>2018-oct-nov</date>
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            <start>1133</start>
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%0 Conference Proceedings
%T Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging
%A Lee, Nayeon
%A Wu, Chien-Sheng
%A Fung, Pascale
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F lee-etal-2018-improving
%X Fact-checking of textual sources needs to effectively extract relevant information from large knowledge bases. In this paper, we extend an existing pipeline approach to better tackle this problem. We propose a neural ranker using a decomposable attention model that dynamically selects sentences to achieve promising improvement in evidence retrieval F1 by 38.80%, with (x65) speedup compared to a TF-IDF method. Moreover, we incorporate lexical tagging methods into our pipeline framework to simplify the tasks and render the model more generalizable. As a result, our framework achieves promising performance on a large-scale fact extraction and verification dataset with speedup.
%R 10.18653/v1/D18-1143
%U https://aclanthology.org/D18-1143
%U https://doi.org/10.18653/v1/D18-1143
%P 1133-1138

Markdown (Informal)

[Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging](https://aclanthology.org/D18-1143) (Lee et al., EMNLP 2018)

• Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging (Lee et al., EMNLP 2018)

ACL

• Nayeon Lee, Chien-Sheng Wu, and Pascale Fung. 2018. Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1133–1138, Brussels, Belgium. Association for Computational Linguistics.