Domain adaptation is a key feature in Machine Translation. It generally encompasses terminology, domain and style adaptation, especially for human post-editing workflows in Computer Assisted Translation (CAT). With Neural Machine Translation (NMT), we introduce a new notion of domain adaptation that we call "specialization" and which is showing promising results both in the learning speed and in adaptation accuracy. In this paper, we propose to explore this approach under several perspectives.
@article{arxiv.1612.06141,
title = {Domain specialization: a post-training domain adaptation for Neural Machine Translation},
author = {Christophe Servan and Josep Crego and Jean Senellart},
journal= {arXiv preprint arXiv:1612.06141},
year = {2016}
}