# Iterative Dual Domain Adaptation for Neural Machine Translation

arXiv: https://arxiv.org/abs/1912.07239 · HTML: https://mlsanity.com/paper/1912.07239/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2019.

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

- [Pruning-then-Expanding Model for Domain Adaptation of Neural Machine Translation](https://mlsanity.com/paper/2103.13678/) (2021)
- [Improving Tree-Structured Decoder Training for Code Generation via Mutual Learning](https://mlsanity.com/paper/2105.14796/) (2021)
- [Towards User-Driven Neural Machine Translation](https://mlsanity.com/paper/2106.06200/) (2021)
- [Continual Learning of Neural Machine Translation within Low Forgetting Risk Regions](https://mlsanity.com/paper/2211.01542/) (2022)
- [A Multi-Agent Framework with Automated Decision Rule Optimization for Cross-Domain Misinformation Detection](https://mlsanity.com/paper/2503.23329/) (2025)
- [Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions](https://mlsanity.com/paper/2302.03950/) (2023)
- [Continual Knowledge Distillation for Neural Machine Translation](https://mlsanity.com/paper/2212.09097/) (2022)
- [Categorizing Semantic Representations for Neural Machine Translation](https://mlsanity.com/paper/2210.06709/) (2022)
- [KD4MT: A Survey of Knowledge Distillation for Machine Translation](https://mlsanity.com/paper/2602.15845/) (2026)
- [Low-resource Language Discrimination Towards Chinese Dialects with Transfer learning and Data Augmentation](https://mlsanity.com/paper/2606.18597/) (2026)

## Abstract (arXiv)

Previous studies on the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of-domain translation knowledge to in-domain NMT model. In this paper, we argue that such a strategy fails to fully extract the domain-shared translation knowledge, and repeatedly utilizing corpora of different domains can lead to better distillation of domain-shared translation knowledge. To this end, we propose an iterative dual domain adaptation framework for NMT. Specifically, we first pre-train in-domain and out-of-domain NMT models using their own training corpora respectively, and then iteratively perform bidirectional translation knowledge transfer (from in-domain to out-of-domain and then vice versa) based on knowledge distillation until the in-domain NMT model convergences. Furthermore, we extend the proposed framework to the scenario of multiple out-of-domain training corpora, where the above-mentioned transfer is performed sequentially between the in-domain and each out-of-domain NMT models in the ascending order of their domain similarities. Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of our framework.