# From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation

arXiv: https://arxiv.org/abs/2412.00966 · HTML: https://mlsanity.com/paper/2412.00966/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 48 references that are in the map.
- First posted on arXiv 1 Dec 2024.

## Builds on (most cited first, 25 of 25)

- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [Simple, Scalable Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1909.08478/) (2019)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translation](https://mlsanity.com/paper/2302.07856/) (2023)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [Domain Control for Neural Machine Translation](https://mlsanity.com/paper/1612.06140/) (2016)
- [Improving Robustness of Machine Translation with Synthetic Noise](https://mlsanity.com/paper/1902.09508/) (2019)
- [Domain Adaptation of Neural Machine Translation by Lexicon Induction](https://mlsanity.com/paper/1906.00376/) (2019)
- [Teaching Large Language Models an Unseen Language on the Fly](https://mlsanity.com/paper/2402.19167/) (2024)
- [Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2105.03953/) (2021)
- [Recipes for Adapting Pre-trained Monolingual and Multilingual Models to Machine Translation](https://mlsanity.com/paper/2004.14911/) (2020)
- [Dynamic Data Selection and Weighting for Iterative Back-Translation](https://mlsanity.com/paper/2004.03672/) (2020)
- [Facilitating Terminology Translation with Target Lemma Annotations](https://mlsanity.com/paper/2101.10035/) (2021)
- [Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translation](https://mlsanity.com/paper/1808.09006/) (2018)
- [A Survey on Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2107.04239/) (2021)
- [Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translation](https://mlsanity.com/paper/2004.02577/) (2020)
- [Adaptation of Machine Translation Models with Back-translated Data using Transductive Data Selection Methods](https://mlsanity.com/paper/1906.07808/) (2019)
- [Incorporating Bilingual Dictionaries for Low Resource Semi-Supervised Neural Machine Translation](https://mlsanity.com/paper/2004.02071/) (2020)
- [Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Tagging](https://mlsanity.com/paper/2102.10160/) (2021)
- [Pointer-Generator Networks for Low-Resource Machine Translation: Don't Copy That!](https://mlsanity.com/paper/2403.10963/) (2024)

## Built on by (most cited first, 1 of 1)

- [AdaptiVocab: Enhancing LLM Efficiency in Focused Domains through Lightweight Vocabulary Adaptation](https://mlsanity.com/paper/2503.19693/) (2025)

## Abstract (arXiv)

Many of the world's languages have insufficient data to train high-performing general neural machine translation (NMT) models, let alone domain-specific models, and often the only available parallel data are small amounts of religious texts. Hence, domain adaptation (DA) is a crucial issue faced by contemporary NMT and has, so far, been underexplored for low-resource languages. In this paper, we evaluate a set of methods from both low-resource NMT and DA in a realistic setting, in which we aim to translate between a high-resource and a low-resource language with access to only: a) parallel Bible data, b) a bilingual dictionary, and c) a monolingual target-domain corpus in the high-resource language. Our results show that the effectiveness of the tested methods varies, with the simplest one, DALI, being most effective. We follow up with a small human evaluation of DALI, which shows that there is still a need for more careful investigation of how to accomplish DA for low-resource NMT.