  The existing neural machine translation system has achieved near human-level
performance in general domain in some languages, but the lack of parallel
corpora poses a key problem in specific domains. In biomedical domain, the
parallel corpus is less accessible. This work presents a new unsupervised
sentence alignment method and explores features in training biomedical neural
machine translation (NMT) systems. We use a simple but effective way to build
bilingual word embeddings (BWEs) to evaluate bilingual word similarity and
transferred the sentence alignment problem into an extended earth mover's
distance (EMD) problem. The proposed method achieved high accuracy in both
1-to-1 and many-to-many cases. Pre-training in general domain, the larger
in-domain dataset and n-to-m sentence pairs benefit the NMT model. Fine-tuning
in domain corpus helps the translation model learns more terminology and fits
the in-domain style of text.
