# Sentence Alignment with Parallel Documents Facilitates Biomedical Machine Translation

arXiv: https://arxiv.org/abs/2104.08588 · HTML: https://mlsanity.com/paper/2104.08588/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 17 Apr 2021.

## Builds on (most cited first, 12 of 12)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [Achieving Human Parity on Automatic Chinese to English News Translation](https://mlsanity.com/paper/1803.05567/) (2018)
- [Neural Machine Translation: A Review and Survey](https://mlsanity.com/paper/1912.02047/) (2019)
- [Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation](https://mlsanity.com/paper/1808.07048/) (2018)
- [Unsupervised Statistical Machine Translation](https://mlsanity.com/paper/1809.01272/) (2018)
- [Multilingual Models for Compositional Distributed Semantics](https://mlsanity.com/paper/1404.4641/) (2014)
- [Don't Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction](https://mlsanity.com/paper/1909.02855/) (2019)
- [BVS Corpus: A Multilingual Parallel Corpus of Biomedical Scientific Texts](https://mlsanity.com/paper/1905.01712/) (2019)
- [NEJM-enzh: A Parallel Corpus for English-Chinese Translation in the Biomedical Domain](https://mlsanity.com/paper/2005.09133/) (2020)

## Built on by (most cited first, 3 of 3)

- [BIOS: An Algorithmically Generated Biomedical Knowledge Graph](https://mlsanity.com/paper/2203.09975/) (2022)
- [CARE-MI: Chinese Benchmark for Misinformation Evaluation in Maternity and Infant Care](https://mlsanity.com/paper/2307.01458/) (2023)
- [AlignAR: Generative Sentence Alignment for Arabic-English Parallel Corpora of Legal and Literary Texts](https://mlsanity.com/paper/2512.21842/) (2025)

## Abstract (arXiv)

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.