<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="puzikov-etal-2019-revisiting">
    <titleInfo>
        <title>Revisiting the Binary Linearization Technique for Surface Realization</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yevgeniy</namePart>
        <namePart type="family">Puzikov</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Claire</namePart>
        <namePart type="family">Gardent</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ido</namePart>
        <namePart type="family">Dagan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Iryna</namePart>
        <namePart type="family">Gurevych</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-oct–nov</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 12th International Conference on Natural Language Generation</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Kees</namePart>
            <namePart type="family">van Deemter</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chenghua</namePart>
            <namePart type="family">Lin</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Hiroya</namePart>
            <namePart type="family">Takamura</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Tokyo, Japan</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>End-to-end neural approaches have achieved state-of-the-art performance in many natural language processing (NLP) tasks. Yet, they often lack transparency of the underlying decision-making process, hindering error analysis and certain model improvements. In this work, we revisit the binary linearization approach to surface realization, which exhibits more interpretable behavior, but was falling short in terms of prediction accuracy. We show how enriching the training data to better capture word order constraints almost doubles the performance of the system. We further demonstrate that encoding both local and global prediction contexts yields another considerable performance boost. With the proposed modifications, the system which ranked low in the latest shared task on multilingual surface realization now achieves best results in five out of ten languages, while being on par with the state-of-the-art approaches in others.</abstract>
    <identifier type="citekey">puzikov-etal-2019-revisiting</identifier>
    <identifier type="doi">10.18653/v1/W19-8635</identifier>
    <location>
        <url>https://aclanthology.org/W19-8635</url>
    </location>
    <part>
        <date>2019-oct–nov</date>
        <extent unit="page">
            <start>268</start>
            <end>278</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Revisiting the Binary Linearization Technique for Surface Realization
%A Puzikov, Yevgeniy
%A Gardent, Claire
%A Dagan, Ido
%A Gurevych, Iryna
%Y van Deemter, Kees
%Y Lin, Chenghua
%Y Takamura, Hiroya
%S Proceedings of the 12th International Conference on Natural Language Generation
%D 2019
%8 oct–nov
%I Association for Computational Linguistics
%C Tokyo, Japan
%F puzikov-etal-2019-revisiting
%X End-to-end neural approaches have achieved state-of-the-art performance in many natural language processing (NLP) tasks. Yet, they often lack transparency of the underlying decision-making process, hindering error analysis and certain model improvements. In this work, we revisit the binary linearization approach to surface realization, which exhibits more interpretable behavior, but was falling short in terms of prediction accuracy. We show how enriching the training data to better capture word order constraints almost doubles the performance of the system. We further demonstrate that encoding both local and global prediction contexts yields another considerable performance boost. With the proposed modifications, the system which ranked low in the latest shared task on multilingual surface realization now achieves best results in five out of ten languages, while being on par with the state-of-the-art approaches in others.
%R 10.18653/v1/W19-8635
%U https://aclanthology.org/W19-8635
%U https://doi.org/10.18653/v1/W19-8635
%P 268-278

Markdown (Informal)

[Revisiting the Binary Linearization Technique for Surface Realization](https://aclanthology.org/W19-8635) (Puzikov et al., INLG 2019)

• Revisiting the Binary Linearization Technique for Surface Realization (Puzikov et al., INLG 2019)

ACL

• Yevgeniy Puzikov, Claire Gardent, Ido Dagan, and Iryna Gurevych. 2019. Revisiting the Binary Linearization Technique for Surface Realization. In Proceedings of the 12th International Conference on Natural Language Generation, pages 268–278, Tokyo, Japan. Association for Computational Linguistics.