Conference paper Open Access

Lexical Micro-adaptation for Neural Machine Translation

Xu, Jitao; Crego, Josep; Senellart, Jean

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Xu, Jitao</dc:creator>
  <dc:creator>Crego, Josep</dc:creator>
  <dc:creator>Senellart, Jean</dc:creator>
  <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:title>Lexical Micro-adaptation for Neural Machine Translation</dc:title>
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