Journal article Open Access

Revisiting Multi-Domain Machine Translation

MinhQuang Pham; Josep Crego; François Yvon


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  <identifier identifierType="DOI">10.5281/zenodo.4537184</identifier>
  <creators>
    <creator>
      <creatorName>MinhQuang Pham</creatorName>
      <affiliation>SYSTRAN, LIMSI/CNRS</affiliation>
    </creator>
    <creator>
      <creatorName>Josep Crego</creatorName>
      <affiliation>SYSTRAN</affiliation>
    </creator>
    <creator>
      <creatorName>François Yvon</creatorName>
      <affiliation>LIMSI/CNRS</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Revisiting Multi-Domain Machine Translation</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Neural Machine Translation</subject>
    <subject>Multi-domain MT</subject>
    <subject>Domain Adaptation</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-02-12</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4537184</alternateIdentifier>
  </alternateIdentifiers>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4537183</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/787061</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;When building machine translation systems, one often needs to make the best out of heterogeneous sets of parallel data in training, and to robustly handle inputs from un-expected domains in testing. This multi-domain scenario has attracted a lot of recent work, that fall under the general umbrella of transfer learning. In this study, we revisit multi-domain machine translation, with the aim to formulate the motivations for developing such systems and the associated expectations with respect to performance. Our experiments with a large sample of multi-domain systems show that most of these expectations are hardly met and suggest that further work is needed to better analyze the current behaviour of multi-domain systems and to make them fully hold their promises.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/787061/">787061</awardNumber>
      <awardTitle>Advanced tools for fighting oNline Illegal TrAfficking</awardTitle>
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