Conference paper Open Access

Lexical Micro-adaptation for Neural Machine Translation

Xu, Jitao; Crego, Josep; Senellart, Jean

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.

Files (432.3 kB)
Name Size
IWSLT2019_paper_9.pdf
md5:b145694f9a8fd725c74837681d966e22
432.3 kB Download
120
91
views
downloads
All versions This version
Views 120120
Downloads 9191
Data volume 39.3 MB39.3 MB
Unique views 102102
Unique downloads 8080

Share

Cite as