NEJM-enzh: A Parallel Corpus for English-Chinese Translation in the Biomedical Domain

TL;DR

This paper introduces NEJM-enzh, a large English-Chinese biomedical parallel corpus, and demonstrates how domain-specific data improves machine translation quality significantly.

Contribution

The creation of a novel, sizable biomedical English-Chinese parallel corpus from NEJM and analysis of domain adaptation effects on translation quality.

Findings

Training on out-of-domain data improves BLEU scores by 25.3/13.4.

Fine-tuning with 4,000 NEJM sentence pairs enhances translation quality.

Larger in-domain datasets further increase BLEU scores by up to 33.0/24.3.

Abstract

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.