# Impact of Domain-Adapted Multilingual Neural Machine Translation in the Medical Domain

arXiv: https://arxiv.org/abs/2212.02143 · HTML: https://mlsanity.com/paper/2212.02143/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [A Survey of Domain Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1806.00258/) (2018)
- [Optimizing Transformer for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2011.02266/) (2020)
- [Continual Learning in Multilingual NMT via Language-Specific Embeddings](https://mlsanity.com/paper/2110.10478/) (2021)
- [A Multilingual Neural Machine Translation Model for Biomedical Data](https://mlsanity.com/paper/2008.02878/) (2020)
- [How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation?](https://mlsanity.com/paper/2208.05225/) (2022)
- [Fine-grained human evaluation of neural versus phrase-based machine translation](https://mlsanity.com/paper/1706.04389/) (2017)

## Abstract (arXiv)

Multilingual Neural Machine Translation (MNMT) models leverage many language pairs during training to improve translation quality for low-resource languages by transferring knowledge from high-resource languages. We study the quality of a domain-adapted MNMT model in the medical domain for English-Romanian with automatic metrics and a human error typology annotation which includes terminology-specific error categories. We compare the out-of-domain MNMT with the in-domain adapted MNMT. The in-domain MNMT model outperforms the out-of-domain MNMT in all measured automatic metrics and produces fewer terminology errors.