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Conference Papers Year : 2019

Lexical Micro-adaptation for Neural Machine Translation

Jitao Xu
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  • PersonId : 184998
  • IdHAL : xujitao
Josep Crego
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Jean Senellart
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  • PersonId : 1038145


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.
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Dates and versions

hal-02635039 , version 1 (27-05-2020)


  • HAL Id : hal-02635039 , version 1


Jitao Xu, Josep Crego, Jean Senellart. Lexical Micro-adaptation for Neural Machine Translation. International Workshop on Spoken Language Translation, Nov 2019, Hong Kong, Hong Kong SAR China. ⟨hal-02635039⟩
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