NEJM-enzh: A Parallel Corpus for English-Chinese Translation in the Biomedical Domain
This addresses a data scarcity problem for researchers and practitioners in biomedical machine translation, though it is incremental as it applies existing methods to a new dataset.
The authors tackled the lack of a parallel corpus for English-Chinese biomedical translation by creating NEJM-enzh, a dataset of about 100,000 sentence pairs, and showed that fine-tuning with as few as 4,000 sentence pairs improved translation quality by up to 25.3 BLEU points.
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.