November 2, 2019 Conference paper Open Access

Xu, Jitao; Crego, Josep; Senellart, Jean

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  <dc:creator>Xu, Jitao</dc:creator>
  <dc:creator>Crego, Josep</dc:creator>
  <dc:creator>Senellart, Jean</dc:creator>
  <dc:date>2019-11-02</dc:date>
  <dc:description>This work is inspired by a typical machine translation industry scenario in which translators make use of in-domain data for facilitating translation of similar or repeating sentences. We introduce a generic framework applied at inference in which a subset of segment pairs are first extracted from training data according to their similarity to the input sentences. These segments are then used to dynamically update the parameters of a generic NMT network, thus performing a lexical micro-adaptation. Our approach demonstrates strong adaptation performance to new and existing datasets including pseudo in-domain data. We evaluate our approach on a heterogeneous English-French training dataset showing accuracy gains on all evaluated domains when compared to strong adaptation baselines.</dc:description>
  <dc:identifier>https://zenodo.org/record/3524977</dc:identifier>
  <dc:identifier>10.5281/zenodo.3524977</dc:identifier>
  <dc:identifier>oai:zenodo.org:3524977</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>doi:10.5281/zenodo.3524976</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/iwslt2019</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>Lexical Micro-adaptation for Neural Machine Translation</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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