# One Sentence One Model for Neural Machine Translation

arXiv: https://arxiv.org/abs/1609.06490 · HTML: https://mlsanity.com/paper/1609.06490/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2016.

## Built on by (most cited first, 7 of 7)

- [A Survey on Retrieval-Augmented Text Generation](https://mlsanity.com/paper/2202.01110/) (2022)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [Fast Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2105.14528/) (2021)
- [TranSmart: A Practical Interactive Machine Translation System](https://mlsanity.com/paper/2105.13072/) (2021)
- [Improving Automated Program Repair with Domain Adaptation](https://mlsanity.com/paper/2212.11414/) (2022)
- [Few-shot learning through contextual data augmentation](https://mlsanity.com/paper/2103.16911/) (2021)
- [Prompting Neural Machine Translation with Translation Memories](https://mlsanity.com/paper/2301.05380/) (2023)

## Abstract (arXiv)

Neural machine translation (NMT) becomes a new state-of-the-art and achieves promising translation results using a simple encoder-decoder neural network. This neural network is trained once on the parallel corpus and the fixed network is used to translate all the test sentences. We argue that the general fixed network cannot best fit the specific test sentences. In this paper, we propose the dynamic NMT which learns a general network as usual, and then fine-tunes the network for each test sentence. The fine-tune work is done on a small set of the bilingual training data that is obtained through similarity search according to the test sentence. Extensive experiments demonstrate that this method can significantly improve the translation performance, especially when highly similar sentences are available.