  Machine translation requires large amounts of parallel text. While such
datasets are abundant in domains such as newswire, they are less accessible in
the biomedical domain. Chinese and English are two of the most widely spoken
languages, yet to our knowledge a parallel corpus in the biomedical domain does
not exist for this language pair. In this study, we develop an effective
pipeline to acquire and process an English-Chinese parallel corpus, consisting
of about 100,000 sentence pairs and 3,000,000 tokens on each side, from the New
England Journal of Medicine (NEJM). We show that training on out-of-domain data
and fine-tuning with as few as 4,000 NEJM sentence pairs improve translation
quality by 25.3 (13.4) BLEU for en$\to$zh (zh$\to$en) directions. Translation
quality continues to improve at a slower pace on larger in-domain datasets,
with an increase of 33.0 (24.3) BLEU for en$\to$zh (zh$\to$en) directions on
the full dataset.
