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A Survey of Domain Adaptation for Neural Machine Translation

arXiv 1806.00258 ↗First posted 1 Jun 2018cs.CL cs.AI cs.LGcited by 64

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Abstract (arXiv)

Neural machine translation (NMT) is a deep learning based approach for machine translation, which yields the state-of-the-art translation performance in scenarios where large-scale parallel corpora are available. Although the high-quality and domain-specific translation is crucial in the real world, domain-specific corpora are usually scarce or nonexistent, and thus vanilla NMT performs poorly in such scenarios. Domain adaptation that leverages both out-of-domain parallel corpora as well as monolingual corpora for in-domain translation, is very important for domain-specific translation. In this paper, we give a comprehensive survey of the state-of-the-art domain adaptation techniques for NMT.

Key facts. Cited by 64 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jun 2018.

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Citing papers per year: 2021: 21; 2022: 16; 2023: 13; 2024: 3; 2025: 6; 2026: 5.

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  1. 2021Neural Machine Translation for Low-Resource Languages: A Surveycited by 108
  2. 2023Diffusion Models for Image Restoration and Enhancement: A Comprehensive Surveycited by 108
  3. 2022State-of-the-art generalisation research in NLP: A taxonomy and reviewcited by 97
  4. 2021Types of Out-of-Distribution Texts and How to Detect Themcited by 80
  5. 2021Neural Machine Translation with Monolingual Translation Memorycited by 61
  6. 2021Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Surveycited by 46
  7. 2021Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translationcited by 28
  8. 2021Learning Kernel-Smoothed Machine Translation with Retrieved Examplescited by 25
  9. 2023Federated Domain Generalization: A Surveycited by 25
  10. 2021TranSmart: A Practical Interactive Machine Translation Systemcited by 17
  11. 2024Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Modelscited by 17
  12. 2021A Survey on Low-Resource Neural Machine Translationcited by 16
38 more
  1. 2021Don't Go Far Off: An Empirical Study on Neural Poetry Translationcited by 15
  2. 2021Sequential Domain Adaptation by Synthesizing Distributionally Robust Expertscited by 14
  3. 2021Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognitioncited by 13
  4. 2021Phrase-level Active Learning for Neural Machine Translationcited by 13
  5. 2021On the Language Coverage Bias for Neural Machine Translationcited by 12
  6. 2022Training Mixed-Domain Translation Models via Federated Learningcited by 12
  7. 2023Simple and Scalable Nearest Neighbor Machine Translationcited by 12
  8. 2021Improving Both Domain Robustness and Domain Adaptability in Machine Translationcited by 11
  9. 2023NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Textscited by 9
  10. 2021Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Taggingcited by 8
  11. 2022When does Parameter-Efficient Transfer Learning Work for Machine Translation?cited by 7
  12. 2022Zero-shot Domain Adaptation for Neural Machine Translation with Retrieved Phrase-level Promptscited by 7
  13. 2025An Interdisciplinary Approach to Human-Centered Machine Translationcited by 6
  14. 2023Federated Nearest Neighbor Machine Translationcited by 5
  15. 2021Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Textscited by 4
  16. 2022Twist Decoding: Diverse Generators Guide Each Othercited by 4
  17. 2023Tailoring Domain Adaptation for Machine Translation Quality Estimationcited by 4
  18. 2023Adaptation Speed Analysis for Fairness-aware Causal Modelscited by 4
  19. 2024Automated Collection of Evaluation Dataset for Semantic Search in Low-Resource Domain Languagecited by 4
  20. 2021Improving Lexically Constrained Neural Machine Translation with Source-Conditioned Masked Span Predictioncited by 3
  21. 2022Balancing Multi-Domain Corpora Learning for Open-Domain Response Generationcited by 3
  22. 2022Domain Adaptation of Machine Translation with Crowdworkerscited by 3
  23. 2025Efficient Domain-adaptive Continual Pretraining for the Process Industry in the German Languagecited by 3
  24. 2026STRUCTSURVEY: Structured Agentic Retrieval for Automated Survey Paper Generationcited by 3
  25. 2021Literature review on vulnerability detection using NLP technologycited by 2
  26. 2022Domain Adaptation in Neural Machine Translation using a Qualia-Enriched FrameNetcited by 2
  27. 2022Time Will Change Things: An Empirical Study on Dynamic Language Understanding in Social Media Classificationcited by 2
  28. 2022m4Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adaptercited by 2
  29. 2021Unsupervised Transfer Learning in Multilingual Neural Machine Translation with Cross-Lingual Word Embeddingscited by 1
  30. 2021Using Machine Translation to Localize Task Oriented NLG Outputcited by 1
  31. 2021Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selectioncited by 1
  32. 2022Global Readiness of Language Technology for Healthcare: What would it Take to Combat the Next Pandemic?cited by 1
  33. 2023Pluggable Neural Machine Translation Models via Memory-augmented Adapterscited by 1
  34. 2023Towards Faster k-Nearest-Neighbor Machine Translationcited by 1
  35. 2024F-MALLOC: Feed-forward Memory Allocation for Continual Learning in Neural Machine Translationcited by 1
  36. 2025English Please: Evaluating Machine Translation with Large Language Models for Multilingual Bug Reportscited by 1
  37. 2025BanglaSTEM: A Parallel Corpus for Technical Domain Bangla-English Translationcited by 1
  38. 2022Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translationcited by 0

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