February 12, 2021 Journal article Open Access

MinhQuang Pham; Josep Crego; François Yvon

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  <dc:creator>MinhQuang Pham</dc:creator>
  <dc:creator>Josep Crego</dc:creator>
  <dc:creator>François Yvon</dc:creator>
  <dc:date>2021-02-12</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://zenodo.org/record/4537184</dc:identifier>
  <dc:identifier>10.5281/zenodo.4537184</dc:identifier>
  <dc:identifier>oai:zenodo.org:4537184</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/787061/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.4537183</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/787061</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:source>Transactions of the Association for Computational Linguistics 9</dc:source>
  <dc:subject>Neural Machine Translation</dc:subject>
  <dc:subject>Multi-domain MT</dc:subject>
  <dc:subject>Domain Adaptation</dc:subject>
  <dc:title>Revisiting Multi-Domain Machine Translation</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
</oai_dc:dc>