Domain specialization: a post-training domain adaptation for Neural Machine Translation
This addresses domain adaptation for Machine Translation, particularly in Computer Assisted Translation workflows, but appears incremental as it builds on existing adaptation concepts.
The paper tackles domain adaptation in Neural Machine Translation by introducing a new concept called 'specialization', which shows promising results in learning speed and adaptation accuracy.
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.