# Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddings

arXiv: https://arxiv.org/abs/1908.10430 · HTML: https://mlsanity.com/paper/1908.10430/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2019.

## Built on by (most cited first, 14 of 14)

- [Adaptive Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2105.13022/) (2021)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation](https://mlsanity.com/paper/2109.06604/) (2021)
- [Simple and Scalable Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2302.12188/) (2023)
- [To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation](https://mlsanity.com/paper/2403.09259/) (2024)
- [AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware Uncertainty Calibrator and Label Distribution Handler](https://mlsanity.com/paper/2407.10784/) (2024)
- [Language Model-Driven Data Pruning Enables Efficient Active Learning](https://mlsanity.com/paper/2410.04275/) (2024)
- [KpopMT: Translation Dataset with Terminology for Kpop Fandom](https://mlsanity.com/paper/2407.07413/) (2024)
- [Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection](https://mlsanity.com/paper/2109.04292/) (2021)
- [Crowdsourcing with Enhanced Data Quality Assurance: An Efficient Approach to Mitigate Resource Scarcity Challenges in Training Large Language Models for Healthcare](https://mlsanity.com/paper/2405.13030/) (2024)
- [Zero-Shot Language Transfer vs Iterative Back Translation for Unsupervised Machine Translation](https://mlsanity.com/paper/2104.00106/) (2021)
- [Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific Rewards](https://mlsanity.com/paper/2310.15259/) (2023)
- [DINA: A Dual Defense Framework Against Internal Noise and External Attacks in Natural Language Processing](https://mlsanity.com/paper/2508.05671/) (2025)
- [Efficient Covariance Estimation for Sparsified Functional Data](https://mlsanity.com/paper/2511.18237/) (2025)

## Abstract (arXiv)

The recent success of neural machine translation models relies on the availability of high quality, in-domain data. Domain adaptation is required when domain-specific data is scarce or nonexistent. Previous unsupervised domain adaptation strategies include training the model with in-domain copied monolingual or back-translated data. However, these methods use generic representations for text regardless of domain shift, which makes it infeasible for translation models to control outputs conditional on a specific domain. In this work, we propose an approach that adapts models with domain-aware feature embeddings, which are learned via an auxiliary language modeling task. Our approach allows the model to assign domain-specific representations to words and output sentences in the desired domain. Our empirical results demonstrate the effectiveness of the proposed strategy, achieving consistent improvements in multiple experimental settings. In addition, we show that combining our method with back translation can further improve the performance of the model.