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

arXiv 2412.00966 ↗First posted 1 Dec 2024cs.CL cs.LGcited by 1

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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.

Key facts. Cited by 1 paper 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

25 of 48 references.

References · 25

  1. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
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13 more
  1. 2019Domain Adaptation of Neural Machine Translation by Lexicon Inductioncited by 33
  2. 2024Teaching Large Language Models an Unseen Language on the Flycited by 30
  3. 2021Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translationcited by 29
  4. 2020Recipes for Adapting Pre-trained Monolingual and Multilingual Models to Machine Translationcited by 28
  5. 2020Dynamic Data Selection and Weighting for Iterative Back-Translationcited by 25
  6. 2021Facilitating Terminology Translation with Target Lemma Annotationscited by 20
  7. 2018Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translationcited by 19
  8. 2021A Survey on Low-Resource Neural Machine Translationcited by 16
  9. 2020Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translationcited by 13
  10. 2019Adaptation of Machine Translation Models with Back-translated Data using Transductive Data Selection Methodscited by 8
  11. 2020Incorporating Bilingual Dictionaries for Low Resource Semi-Supervised Neural Machine Translationcited by 8
  12. 2021Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Taggingcited by 8
  13. 2024Pointer-Generator Networks for Low-Resource Machine Translation: Don't Copy That!cited by 1

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  1. 2025AdaptiVocab: Enhancing LLM Efficiency in Focused Domains through Lightweight Vocabulary Adaptationcited by 15