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        <title>Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation</title>
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        <namePart type="given">Masashi</namePart>
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    <name type="personal">
        <namePart type="given">Daisuke</namePart>
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    <abstract>We propose a new domain adaptation method for Combinatory Categorial Grammar (CCG) parsing, based on the idea of automatic generation of CCG corpora exploiting cheaper resources of dependency trees. Our solution is conceptually simple, and not relying on a specific parser architecture, making it applicable to the current best-performing parsers. We conduct extensive parsing experiments with detailed discussion; on top of existing benchmark datasets on (1) biomedical texts and (2) question sentences, we create experimental datasets of (3) speech conversation and (4) math problems. When applied to the proposed method, an off-the-shelf CCG parser shows significant performance gains, improving from 90.7% to 96.6% on speech conversation, and from 88.5% to 96.8% on math problems.</abstract>
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%0 Conference Proceedings
%T Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation
%A Yoshikawa, Masashi
%A Noji, Hiroshi
%A Mineshima, Koji
%A Bekki, Daisuke
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F yoshikawa-etal-2019-automatic
%X We propose a new domain adaptation method for Combinatory Categorial Grammar (CCG) parsing, based on the idea of automatic generation of CCG corpora exploiting cheaper resources of dependency trees. Our solution is conceptually simple, and not relying on a specific parser architecture, making it applicable to the current best-performing parsers. We conduct extensive parsing experiments with detailed discussion; on top of existing benchmark datasets on (1) biomedical texts and (2) question sentences, we create experimental datasets of (3) speech conversation and (4) math problems. When applied to the proposed method, an off-the-shelf CCG parser shows significant performance gains, improving from 90.7% to 96.6% on speech conversation, and from 88.5% to 96.8% on math problems.
%R 10.18653/v1/P19-1013
%U https://aclanthology.org/P19-1013
%U https://doi.org/10.18653/v1/P19-1013
%P 129-139

Markdown (Informal)

[Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation](https://aclanthology.org/P19-1013) (Yoshikawa et al., ACL 2019)

• Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation (Yoshikawa et al., ACL 2019)

ACL

• Masashi Yoshikawa, Hiroshi Noji, Koji Mineshima, and Daisuke Bekki. 2019. Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 129–139, Florence, Italy. Association for Computational Linguistics.