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.
@article{arxiv.2412.00966,
title = {From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation},
author = {Ali Marashian and Enora Rice and Luke Gessler and Alexis Palmer and Katharina von der Wense},
journal= {arXiv preprint arXiv:2412.00966},
year = {2025}
}