3524977
doi
10.5281/zenodo.3524977
oai:zenodo.org:3524977
user-iwslt2019
Crego, Josep
SYSTRAN, 5 rue Feydeau, 75002 Paris (France)
Senellart, Jean
SYSTRAN, 5 rue Feydeau, 75002 Paris (France)
Lexical Micro-adaptation for Neural Machine Translation
Xu, Jitao
SYSTRAN, 5 rue Feydeau, 75002 Paris (France)
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
<p>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.</p>
Zenodo
2019-11-02
info:eu-repo/semantics/conferencePaper
3524976
user-iwslt2019
1579540594.832476
432333
md5:b145694f9a8fd725c74837681d966e22
https://zenodo.org/records/3524977/files/IWSLT2019_paper_9.pdf
public
10.5281/zenodo.3524976
isVersionOf
doi