November 2, 2019 Conference paper Open Access

Macé, Valentin; Servan, Christophe

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  <identifier identifierType="DOI">10.5281/zenodo.3525020</identifier>
  <creators>
    <creator>
      <creatorName>Macé, Valentin</creatorName>
      <givenName>Valentin</givenName>
      <familyName>Macé</familyName>
      <affiliation>QWANT RESEARCH - 7 Rue Spontini, 75116 Paris, France</affiliation>
    </creator>
    <creator>
      <creatorName>Servan, Christophe</creatorName>
      <givenName>Christophe</givenName>
      <familyName>Servan</familyName>
      <affiliation>QWANT RESEARCH - 7 Rue Spontini, 75116 Paris, France</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Using Whole Document Context in Neural Machine Translation</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-11-02</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3525020</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3525019</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/iwslt2019</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;In Machine Translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a simple yet promising approach to add contextual information in Neural Machine Translation. We present a method to add source context that capture the whole document with accurate boundaries, taking every word into account. We provide this additional information to a Transformer model and study the impact of our method on three language pairs. The proposed approach obtains promising results in the English-German, English-French and French-English document-level translation tasks. We observe interesting cross-sentential behaviors where the model learns to use document-level information to improve translation coherence.&lt;/p&gt;</description>
  </descriptions>
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