  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.
