Sentence Alignment with Parallel Documents Facilitates Biomedical Machine Translation

TL;DR

This paper introduces an unsupervised sentence alignment algorithm that creates biomedical parallel corpora from document translations, significantly improving neural machine translation quality in specialized biomedical domains.

Contribution

It presents a novel unsupervised method for aligning sentences in biomedical texts, enabling the creation of high-quality parallel corpora for NMT training without manual annotation.

Findings

Accurate sentence alignment achieved in 1-to-1 cases.

Outperformed competing algorithms in many-to-many alignments.

Biomedical NMT models improved BLEU scores by over 17 points after fine-tuning.

Abstract

Objective: Today's neural machine translation (NMT) can achieve near human-level translation quality and greatly facilitates international communications, but the lack of parallel corpora poses a key problem to the development of translation systems for highly specialized domains, such as biomedicine. This work presents an unsupervised algorithm for deriving parallel corpora from document-level translations by using sentence alignment and explores how training materials affect the performance of biomedical NMT systems. Materials and Methods: Document-level translations are mixed to train bilingual word embeddings (BWEs) for the evaluation of cross-lingual word similarity, and sentence distance is defined by combining semantic and positional similarities of the sentences. The alignment of sentences is formulated as an extended earth mover's distance problem. A Chinese-English biomedical parallel corpus is derived with the proposed algorithm using bilingual articles from UpToDate and translations of PubMed abstracts, which is then used for the training and evaluation of NMT. Results: On two manually aligned translation datasets, the proposed algorithm achieved accurate sentence alignment in the 1-to-1 cases and outperformed competing algorithms in the many-to-many cases. The NMT model fine-tuned on biomedical data significantly improved the in-domain translation quality (zh-en: +17.72 BLEU; en-zh: +17.02 BLEU). Both the size of the training data and the combination of different corpora can significantly affect the model's performance. Conclusion: The proposed algorithm relaxes the assumption for sentence alignment and effectively generates accurate translation pairs that facilitate training high quality biomedical NMT models.