Lexical Micro-adaptation for Neural Machine Translation

Jitao Xu (1) , Josep Crego (1) , Jean Senellart (1)

1 SYSTRAN

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.

Domaines: Informatique et langage [cs.CL]

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⟩