mlsanityData to 7 Oct 2026

Domain Control for Neural Machine Translation

arXiv 1612.06140 ↗First posted 19 Dec 2016cs.CLcited by 36

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Abstract (arXiv)

Machine translation systems are very sensitive to the domains they were trained on. Several domain adaptation techniques have been deeply studied. We propose a new technique for neural machine translation (NMT) that we call domain control which is performed at runtime using a unique neural network covering multiple domains. The presented approach shows quality improvements when compared to dedicated domains translating on any of the covered domains and even on out-of-domain data. In addition, model parameters do not need to be re-estimated for each domain, making this effective to real use cases. Evaluation is carried out on English-to-French translation for two different testing scenarios. We first consider the case where an end-user performs translations on a known domain. Secondly, we consider the scenario where the domain is not known and predicted at the sentence level before translating. Results show consistent accuracy improvements for both conditions.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Dec 2016.

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Citing papers per year: 2021: 13; 2022: 9; 2023: 6; 2024: 4; 2025: 4.

36 papers.

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  1. 2021Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Surveycited by 46
  2. 2023Mitigating Hallucinations and Off-target Machine Translation with Source-Contrastive and Language-Contrastive Decodingcited by 42
  3. 2021Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfercited by 28
  4. 2021On Generalization in Coreference Resolutioncited by 24
  5. 2022PoeLM: A Meter- and Rhyme-Controllable Language Model for Unsupervised Poetry Generationcited by 22
  6. 2021Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapterscited by 21
  7. 2021Controllable Summarization with Constrained Markov Decision Processcited by 17
  8. 2023Domain Terminology Integration into Machine Translation: Leveraging Large Language Modelscited by 17
  9. 2021Pruning-then-Expanding Model for Domain Adaptation of Neural Machine Translationcited by 15
  10. 2022Controllable Image Captioning via Promptingcited by 15
  11. 2022Domain-Specific Text Generation for Machine Translationcited by 12
  12. 2021Continuous Learning in Neural Machine Translation using Bilingual Dictionariescited by 11
24 more
  1. 2021Improving Both Domain Robustness and Domain Adaptability in Machine Translationcited by 11
  2. 2023Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Modelcited by 9
  3. 2021Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Taggingcited by 8
  4. 2023Tagged End-to-End Simultaneous Speech Translation Training using Simultaneous Interpretation Datacited by 6
  5. 2021Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modelingcited by 5
  6. 2021Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Textscited by 4
  7. 2021HintedBT: Augmenting Back-Translation with Quality and Transliteration Hintscited by 3
  8. 2022Entailment Relation Aware Paraphrase Generationcited by 3
  9. 2024Generating Gender Alternatives in Machine Translationcited by 3
  10. 2025DMDTEval: An Evaluation and Analysis of LLMs on Disambiguation in Multi-domain Translationcited by 3
  11. 2022Multi-Figurative Language Generationcited by 2
  12. 2022m4Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adaptercited by 2
  13. 2023How Transferable are Attribute Controllers on Pretrained Multilingual Translation Models?cited by 2
  14. 2024Fine-grained length controllable video captioning with ordinal embeddingscited by 2
  15. 2025Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markerscited by 2
  16. 2021Domain Controlled Title Generation with Human Evaluationcited by 1
  17. 2022Can Domains Be Transferred Across Languages in Multi-Domain Multilingual Neural Machine Translation?cited by 1
  18. 2022Specializing Multi-domain NMT via Penalizing Low Mutual Informationcited by 1
  19. 2024Investigating the potential of Sparse Mixtures-of-Experts for multi-domain neural machine translationcited by 1
  20. 2024From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translationcited by 1
  21. 2022Additive Interventions Yield Robust Multi-Domain Machine Translation Modelscited by 0
  22. 2023Label-Free Multi-Domain Machine Translation with Stage-wise Trainingcited by 0
  23. 2025In-Domain African Languages Translation Using LLMs and Multi-armed Banditscited by 0
  24. 2025Optimal Corpus Aware Training for Neural Machine Translationcited by 0