# Fast Domain Adaptation for Neural Machine Translation

arXiv: https://arxiv.org/abs/1612.06897 · HTML: https://mlsanity.com/paper/1612.06897/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2016.

## Built on by (most cited first, 28 of 28)

- [In-context Examples Selection for Machine Translation](https://mlsanity.com/paper/2212.02437/) (2022)
- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning](https://mlsanity.com/paper/1805.10627/) (2018)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods](https://mlsanity.com/paper/2309.10966/) (2023)
- [Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech](https://mlsanity.com/paper/2109.06952/) (2021)
- [Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapters](https://mlsanity.com/paper/2110.09574/) (2021)
- [Pruning-then-Expanding Model for Domain Adaptation of Neural Machine Translation](https://mlsanity.com/paper/2103.13678/) (2021)
- [Improving Both Domain Robustness and Domain Adaptability in Machine Translation](https://mlsanity.com/paper/2112.08288/) (2021)
- [Data Selection Curriculum for Neural Machine Translation](https://mlsanity.com/paper/2203.13867/) (2022)
- [Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions](https://mlsanity.com/paper/2210.05047/) (2022)
- [Text-to-SQL Domain Adaptation via Human-LLM Collaborative Data Annotation](https://mlsanity.com/paper/2502.15980/) (2025)
- [Leveraging Domain Knowledge at Inference Time for LLM Translation: Retrieval versus Generation](https://mlsanity.com/paper/2503.05010/) (2025)
- [A Systematic Analysis of Vocabulary and BPE Settings for Optimal Fine-tuning of NMT: A Case Study of In-domain Translation](https://mlsanity.com/paper/2303.00722/) (2023)
- [DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation](https://mlsanity.com/paper/2204.09259/) (2022)
- [Improving Robustness of Retrieval Augmented Translation via Shuffling of Suggestions](https://mlsanity.com/paper/2210.05059/) (2022)
- [m4Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter](https://mlsanity.com/paper/2210.11912/) (2022)
- [How Transferable are Attribute Controllers on Pretrained Multilingual Translation Models?](https://mlsanity.com/paper/2309.08565/) (2023)
- [Learning Policies for Multilingual Training of Neural Machine Translation Systems](https://mlsanity.com/paper/2103.06964/) (2021)
- [Using Machine Translation to Localize Task Oriented NLG Output](https://mlsanity.com/paper/2107.04512/) (2021)
- [Can Domains Be Transferred Across Languages in Multi-Domain Multilingual Neural Machine Translation?](https://mlsanity.com/paper/2210.11628/) (2022)
- [Synslator: An Interactive Machine Translation Tool with Online Learning](https://mlsanity.com/paper/2310.05025/) (2023)
- [F-MALLOC: Feed-forward Memory Allocation for Continual Learning in Neural Machine Translation](https://mlsanity.com/paper/2404.04846/) (2024)
- [SemiAdapt and SemiLoRA: Efficient Domain Adaptation for Transformer-based Low-Resource Language Translation with a Case Study on Irish](https://mlsanity.com/paper/2510.18725/) (2025)
- [Redefining Machine Simultaneous Interpretation: From Incremental Translation to Human-Like Strategies](https://mlsanity.com/paper/2601.11002/) (2026)
- [Domain Specific Sub-network for Multi-Domain Neural Machine Translation](https://mlsanity.com/paper/2210.09805/) (2022)
- [Soft Alignment Objectives for Robust Adaptation of Language Generation](https://mlsanity.com/paper/2211.16550/) (2022)
- [Redefining Machine Simultaneous Interpretation: From Incremental Translation to Human-Like Strategies](https://mlsanity.com/paper/2509.21801/) (2025)

## Abstract (arXiv)

Neural Machine Translation (NMT) is a new approach for automatic translation of text from one human language into another. The basic concept in NMT is to train a large Neural Network that maximizes the translation performance on a given parallel corpus. NMT is gaining popularity in the research community because it outperformed traditional SMT approaches in several translation tasks at WMT and other evaluation tasks/benchmarks at least for some language pairs. However, many of the enhancements in SMT over the years have not been incorporated into the NMT framework. In this paper, we focus on one such enhancement namely domain adaptation. We propose an approach for adapting a NMT system to a new domain. The main idea behind domain adaptation is that the availability of large out-of-domain training data and a small in-domain training data. We report significant gains with our proposed method in both automatic metrics and a human subjective evaluation metric on two language pairs. With our adaptation method, we show large improvement on the new domain while the performance of our general domain only degrades slightly. In addition, our approach is fast enough to adapt an already trained system to a new domain within few hours without the need to retrain the NMT model on the combined data which usually takes several days/weeks depending on the volume of the data.