<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kanayama-etal-2018-neural">
    <titleInfo>
        <title>A neural parser as a direct classifier for head-final languages</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hiroshi</namePart>
        <namePart type="family">Kanayama</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Masayasu</namePart>
        <namePart type="family">Muraoka</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ryosuke</namePart>
        <namePart type="family">Kohita</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 Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Georgiana</namePart>
            <namePart type="family">Dinu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Miguel</namePart>
            <namePart type="family">Ballesteros</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Avirup</namePart>
            <namePart type="family">Sil</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sam</namePart>
            <namePart type="family">Bowman</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Wael</namePart>
            <namePart type="family">Hamza</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Anders</namePart>
            <namePart type="family">Sogaard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tahira</namePart>
            <namePart type="family">Naseem</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yoav</namePart>
            <namePart type="family">Goldberg</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>This paper demonstrates a neural parser implementation suitable for consistently head-final languages such as Japanese. Unlike the transition- and graph-based algorithms in most state-of-the-art parsers, our parser directly selects the head word of a dependent from a limited number of candidates. This method drastically simplifies the model so that we can easily interpret the output of the neural model. Moreover, by exploiting grammatical knowledge to restrict possible modification types, we can control the output of the parser to reduce specific errors without adding annotated corpora. The neural parser performed well both on conventional Japanese corpora and the Japanese version of Universal Dependency corpus, and the advantages of distributed representations were observed in the comparison with the non-neural conventional model.</abstract>
    <identifier type="citekey">kanayama-etal-2018-neural</identifier>
    <identifier type="doi">10.18653/v1/W18-2906</identifier>
    <location>
        <url>https://aclanthology.org/W18-2906</url>
    </location>
    <part>
        <date>2018-07</date>
        <extent unit="page">
            <start>38</start>
            <end>46</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A neural parser as a direct classifier for head-final languages
%A Kanayama, Hiroshi
%A Muraoka, Masayasu
%A Kohita, Ryosuke
%Y Dinu, Georgiana
%Y Ballesteros, Miguel
%Y Sil, Avirup
%Y Bowman, Sam
%Y Hamza, Wael
%Y Sogaard, Anders
%Y Naseem, Tahira
%Y Goldberg, Yoav
%S Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F kanayama-etal-2018-neural
%X This paper demonstrates a neural parser implementation suitable for consistently head-final languages such as Japanese. Unlike the transition- and graph-based algorithms in most state-of-the-art parsers, our parser directly selects the head word of a dependent from a limited number of candidates. This method drastically simplifies the model so that we can easily interpret the output of the neural model. Moreover, by exploiting grammatical knowledge to restrict possible modification types, we can control the output of the parser to reduce specific errors without adding annotated corpora. The neural parser performed well both on conventional Japanese corpora and the Japanese version of Universal Dependency corpus, and the advantages of distributed representations were observed in the comparison with the non-neural conventional model.
%R 10.18653/v1/W18-2906
%U https://aclanthology.org/W18-2906
%U https://doi.org/10.18653/v1/W18-2906
%P 38-46

Markdown (Informal)

[A neural parser as a direct classifier for head-final languages](https://aclanthology.org/W18-2906) (Kanayama et al., ACL 2018)

• A neural parser as a direct classifier for head-final languages (Kanayama et al., ACL 2018)

ACL

• Hiroshi Kanayama, Masayasu Muraoka, and Ryosuke Kohita. 2018. A neural parser as a direct classifier for head-final languages. In Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP, pages 38–46, Melbourne, Australia. Association for Computational Linguistics.