# A Multilingual Neural Machine Translation Model for Biomedical Data

arXiv: https://arxiv.org/abs/2008.02878 · HTML: https://mlsanity.com/paper/2008.02878/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 7 of 7)

- [Transformers in Healthcare: A Survey](https://mlsanity.com/paper/2307.00067/) (2023)
- [On the Evaluation of Machine Translation for Terminology Consistency](https://mlsanity.com/paper/2106.11891/) (2021)
- [Natural Language Processing for Smart Healthcare](https://mlsanity.com/paper/2110.15803/) (2021)
- [Do Multilingual Neural Machine Translation Models Contain Language Pair Specific Attention Heads?](https://mlsanity.com/paper/2105.14940/) (2021)
- [Family of Origin and Family of Choice: Massively Parallel Lexiconized Iterative Pretraining for Severely Low Resource Machine Translation](https://mlsanity.com/paper/2104.05848/) (2021)
- [Can Domains Be Transferred Across Languages in Multi-Domain Multilingual Neural Machine Translation?](https://mlsanity.com/paper/2210.11628/) (2022)
- [Impact of Domain-Adapted Multilingual Neural Machine Translation in the Medical Domain](https://mlsanity.com/paper/2212.02143/) (2022)

## Abstract (arXiv)

We release a multilingual neural machine translation model, which can be used to translate text in the biomedical domain. The model can translate from 5 languages (French, German, Italian, Korean and Spanish) into English. It is trained with large amounts of generic and biomedical data, using domain tags. Our benchmarks show that it performs near state-of-the-art both on news (generic domain) and biomedical test sets, and that it outperforms the existing publicly released models. We believe that this release will help the large-scale multilingual analysis of the digital content of the COVID-19 crisis and of its effects on society, economy, and healthcare policies. We also release a test set of biomedical text for Korean-English. It consists of 758 sentences from official guidelines and recent papers, all about COVID-19.