# Domain Differential Adaptation for Neural Machine Translation

arXiv: https://arxiv.org/abs/1910.02555 · HTML: https://mlsanity.com/paper/1910.02555/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Tuning Language Models by Proxy](https://mlsanity.com/paper/2401.08565/) (2024)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [Selective Prompt Anchoring for Code Generation](https://mlsanity.com/paper/2408.09121/) (2024)
- [Delta Sampling: Data-Free Knowledge Transfer Across Diffusion Models](https://mlsanity.com/paper/2512.03056/) (2025)
- [CPT: Consistent Proxy Tuning for Black-box Optimization](https://mlsanity.com/paper/2407.01155/) (2024)
- [Advanced Black-Box Tuning of Large Language Models with Limited API Calls](https://mlsanity.com/paper/2511.10210/) (2025)
- [Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation](https://mlsanity.com/paper/2206.01137/) (2022)
- [Domain Curricula for Code-Switched MT at MixMT 2022](https://mlsanity.com/paper/2210.17463/) (2022)
- [Efficient Covariance Estimation for Sparsified Functional Data](https://mlsanity.com/paper/2511.18237/) (2025)

## Abstract (arXiv)

Neural networks are known to be data hungry and domain sensitive, but it is nearly impossible to obtain large quantities of labeled data for every domain we are interested in. This necessitates the use of domain adaptation strategies. One common strategy encourages generalization by aligning the global distribution statistics between source and target domains, but one drawback is that the statistics of different domains or tasks are inherently divergent, and smoothing over these differences can lead to sub-optimal performance. In this paper, we propose the framework of it Domain Differential Adaptation (DDA), where instead of smoothing over these differences we embrace them, directly modeling the difference between domains using models in a related task. We then use these learned domain differentials to adapt models for the target task accordingly. Experimental results on domain adaptation for neural machine translation demonstrate the effectiveness of this strategy, achieving consistent improvements over other alternative adaptation strategies in multiple experimental settings.