<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kurita-etal-2018-neural">
    <titleInfo>
        <title>Neural Adversarial Training for Semi-supervised Japanese Predicate-argument Structure Analysis</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Shuhei</namePart>
        <namePart type="family">Kurita</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Daisuke</namePart>
        <namePart type="family">Kawahara</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sadao</namePart>
        <namePart type="family">Kurohashi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Melbourne, Australia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Japanese predicate-argument structure (PAS) analysis involves zero anaphora resolution, which is notoriously difficult. To improve the performance of Japanese PAS analysis, it is straightforward to increase the size of corpora annotated with PAS. However, since it is prohibitively expensive, it is promising to take advantage of a large amount of raw corpora. In this paper, we propose a novel Japanese PAS analysis model based on semi-supervised adversarial training with a raw corpus. In our experiments, our model outperforms existing state-of-the-art models for Japanese PAS analysis.</abstract>
    <identifier type="citekey">kurita-etal-2018-neural</identifier>
    <identifier type="doi">10.18653/v1/P18-1044</identifier>
    <location>
        <url>https://aclanthology.org/P18-1044</url>
    </location>
    <part>
        <date>2018-07</date>
        <extent unit="page">
            <start>474</start>
            <end>484</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Neural Adversarial Training for Semi-supervised Japanese Predicate-argument Structure Analysis
%A Kurita, Shuhei
%A Kawahara, Daisuke
%A Kurohashi, Sadao
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F kurita-etal-2018-neural
%X Japanese predicate-argument structure (PAS) analysis involves zero anaphora resolution, which is notoriously difficult. To improve the performance of Japanese PAS analysis, it is straightforward to increase the size of corpora annotated with PAS. However, since it is prohibitively expensive, it is promising to take advantage of a large amount of raw corpora. In this paper, we propose a novel Japanese PAS analysis model based on semi-supervised adversarial training with a raw corpus. In our experiments, our model outperforms existing state-of-the-art models for Japanese PAS analysis.
%R 10.18653/v1/P18-1044
%U https://aclanthology.org/P18-1044
%U https://doi.org/10.18653/v1/P18-1044
%P 474-484

Markdown (Informal)

[Neural Adversarial Training for Semi-supervised Japanese Predicate-argument Structure Analysis](https://aclanthology.org/P18-1044) (Kurita et al., ACL 2018)

• Neural Adversarial Training for Semi-supervised Japanese Predicate-argument Structure Analysis (Kurita et al., ACL 2018)

ACL

• Shuhei Kurita, Daisuke Kawahara, and Sadao Kurohashi. 2018. Neural Adversarial Training for Semi-supervised Japanese Predicate-argument Structure Analysis. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 474–484, Melbourne, Australia. Association for Computational Linguistics.