Domain Adaptation in Neural Machine Translation

Domain Adaptation is useful for specializing current generic Machine Translation models, mainly when the specialized corpus is too limited to train a separate model. Furthermore, Domain Adaptation techniques can be handy for low-resource languages that share vocabulary and structure with other rich-resource family languages.

As part of my Machine Translation research, I managed to achieve successful results in retraining Neural Machine Translation models for the purpose of Domain Adaptation using OpenNMT-py (the PyTorch version of OpenNMT). In this article, I am elaborating on the path I took and the achieved outcomes; hopefully, this will be useful for others.

Continue reading “Domain Adaptation in Neural Machine Translation”