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    <titleInfo>
        <title>Acquisition of Phrase Correspondences Using Natural Deduction Proofs</title>
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    <name type="personal">
        <namePart type="given">Hitomi</namePart>
        <namePart type="family">Yanaka</namePart>
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    <name type="personal">
        <namePart type="given">Koji</namePart>
        <namePart type="family">Mineshima</namePart>
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    <name type="personal">
        <namePart type="given">Pascual</namePart>
        <namePart type="family">Martínez-Gómez</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Daisuke</namePart>
        <namePart type="family">Bekki</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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        <dateIssued>2018-06</dateIssued>
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            <title>Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)</title>
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            <namePart type="given">Marilyn</namePart>
            <namePart type="family">Walker</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Heng</namePart>
            <namePart type="family">Ji</namePart>
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        <name type="personal">
            <namePart type="given">Amanda</namePart>
            <namePart type="family">Stent</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>How to identify, extract, and use phrasal knowledge is a crucial problem for the task of Recognizing Textual Entailment (RTE). To solve this problem, we propose a method for detecting paraphrases via natural deduction proofs of semantic relations between sentence pairs. Our solution relies on a graph reformulation of partial variable unifications and an algorithm that induces subgraph alignments between meaning representations. Experiments show that our method can automatically detect various paraphrases that are absent from existing paraphrase databases. In addition, the detection of paraphrases using proof information improves the accuracy of RTE tasks.</abstract>
    <identifier type="citekey">yanaka-etal-2018-acquisition</identifier>
    <identifier type="doi">10.18653/v1/N18-1069</identifier>
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        <date>2018-06</date>
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%0 Conference Proceedings
%T Acquisition of Phrase Correspondences Using Natural Deduction Proofs
%A Yanaka, Hitomi
%A Mineshima, Koji
%A Martínez-Gómez, Pascual
%A Bekki, Daisuke
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F yanaka-etal-2018-acquisition
%X How to identify, extract, and use phrasal knowledge is a crucial problem for the task of Recognizing Textual Entailment (RTE). To solve this problem, we propose a method for detecting paraphrases via natural deduction proofs of semantic relations between sentence pairs. Our solution relies on a graph reformulation of partial variable unifications and an algorithm that induces subgraph alignments between meaning representations. Experiments show that our method can automatically detect various paraphrases that are absent from existing paraphrase databases. In addition, the detection of paraphrases using proof information improves the accuracy of RTE tasks.
%R 10.18653/v1/N18-1069
%U https://aclanthology.org/N18-1069
%U https://doi.org/10.18653/v1/N18-1069
%P 756-766

Markdown (Informal)

[Acquisition of Phrase Correspondences Using Natural Deduction Proofs](https://aclanthology.org/N18-1069) (Yanaka et al., NAACL 2018)

• Acquisition of Phrase Correspondences Using Natural Deduction Proofs (Yanaka et al., NAACL 2018)

ACL

• Hitomi Yanaka, Koji Mineshima, Pascual Martínez-Gómez, and Daisuke Bekki. 2018. Acquisition of Phrase Correspondences Using Natural Deduction Proofs. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 756–766, New Orleans, Louisiana. Association for Computational Linguistics.