<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hardmeier-federico-2010-modelling">
    <titleInfo>
        <title>Modelling pronominal anaphora in statistical machine translation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Christian</namePart>
        <namePart type="family">Hardmeier</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Marcello</namePart>
        <namePart type="family">Federico</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2010-dec 2-3</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 7th International Workshop on Spoken Language Translation: Papers</title>
        </titleInfo>
        <originInfo>
            <place>
                <placeTerm type="text">Paris, France</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Current Statistical Machine Translation (SMT) systems translate texts sentence by sentence without considering any cross-sentential context. Assuming independence between sentences makes it difficult to take certain translation decisions when the necessary information cannot be determined locally. We argue for the necessity to include crosssentence dependencies in SMT. As a case in point, we study the problem of pronominal anaphora translation by manually evaluating German-English SMT output. We then present a word dependency model for SMT, which can represent links between word pairs in the same or in different sentences. We use this model to integrate the output of a coreference resolution system into English-German SMT with a view to improving the translation of anaphoric pronouns.</abstract>
    <identifier type="citekey">hardmeier-federico-2010-modelling</identifier>
    <location>
        <url>https://aclanthology.org/2010.iwslt-papers.10</url>
    </location>
    <part>
        <date>2010-dec 2-3</date>
        <extent unit="page">
            <start>283</start>
            <end>289</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Modelling pronominal anaphora in statistical machine translation
%A Hardmeier, Christian
%A Federico, Marcello
%S Proceedings of the 7th International Workshop on Spoken Language Translation: Papers
%D 2010
%8 dec 2 3
%C Paris, France
%F hardmeier-federico-2010-modelling
%X Current Statistical Machine Translation (SMT) systems translate texts sentence by sentence without considering any cross-sentential context. Assuming independence between sentences makes it difficult to take certain translation decisions when the necessary information cannot be determined locally. We argue for the necessity to include crosssentence dependencies in SMT. As a case in point, we study the problem of pronominal anaphora translation by manually evaluating German-English SMT output. We then present a word dependency model for SMT, which can represent links between word pairs in the same or in different sentences. We use this model to integrate the output of a coreference resolution system into English-German SMT with a view to improving the translation of anaphoric pronouns.
%U https://aclanthology.org/2010.iwslt-papers.10
%P 283-289

Markdown (Informal)

[Modelling pronominal anaphora in statistical machine translation](https://aclanthology.org/2010.iwslt-papers.10) (Hardmeier & Federico, IWSLT 2010)

• Modelling pronominal anaphora in statistical machine translation (Hardmeier & Federico, IWSLT 2010)

ACL

• Christian Hardmeier and Marcello Federico. 2010. Modelling pronominal anaphora in statistical machine translation. In Proceedings of the 7th International Workshop on Spoken Language Translation: Papers, pages 283–289, Paris, France.