# An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation

arXiv: https://arxiv.org/abs/1701.03214 · HTML: https://mlsanity.com/paper/1701.03214/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2017.

## Built on by (most cited first, 3 of 3)

- [Pruning-then-Expanding Model for Domain Adaptation of Neural Machine Translation](https://mlsanity.com/paper/2103.13678/) (2021)
- [Don't Go Far Off: An Empirical Study on Neural Poetry Translation](https://mlsanity.com/paper/2109.02972/) (2021)
- [Continual Learning of Neural Machine Translation within Low Forgetting Risk Regions](https://mlsanity.com/paper/2211.01542/) (2022)

## Abstract (arXiv)

In this paper, we propose a novel domain adaptation method named "mixed fine tuning" for neural machine translation (NMT). We combine two existing approaches namely fine tuning and multi domain NMT. We first train an NMT model on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus which is a mix of the in-domain and out-of-domain corpora. All corpora are augmented with artificial tags to indicate specific domains. We empirically compare our proposed method against fine tuning and multi domain methods and discuss its benefits and shortcomings.