# Prompting Neural Machine Translation with Translation Memories

arXiv: https://arxiv.org/abs/2301.05380 · HTML: https://mlsanity.com/paper/2301.05380/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 13 Jan 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2010.00710/) (2020)
- [Facebook FAIR's WMT19 News Translation Task Submission](https://mlsanity.com/paper/1907.06616/) (2019)
- [Unsupervised Domain Clusters in Pretrained Language Models](https://mlsanity.com/paper/2004.02105/) (2020)
- [Guiding Neural Machine Translation with Retrieved Translation Pieces](https://mlsanity.com/paper/1804.02559/) (2018)
- [Neural Machine Translation with Monolingual Translation Memory](https://mlsanity.com/paper/2105.11269/) (2021)
- [Adaptive Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2105.13022/) (2021)
- [Fast Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2105.14528/) (2021)
- [Non-Parametric Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1903.00058/) (2019)
- [Chunk-based Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2205.12230/) (2022)
- [Mask-Align: Self-Supervised Neural Word Alignment](https://mlsanity.com/paper/2012.07162/) (2020)
- [Faster Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2112.08152/) (2021)
- [One Sentence One Model for Neural Machine Translation](https://mlsanity.com/paper/1609.06490/) (2016)
- [Learning to Reuse Translations: Guiding Neural Machine Translation with Examples](https://mlsanity.com/paper/1911.10732/) (2019)

## Built on by (most cited first, 2 of 2)

- [Augmenting Large Language Model Translators via Translation Memories](https://mlsanity.com/paper/2305.17367/) (2023)
- [Improving Retrieval-Augmented Neural Machine Translation with Monolingual Data](https://mlsanity.com/paper/2504.21747/) (2025)

## Abstract (arXiv)

Improving machine translation (MT) systems with translation memories (TMs) is of great interest to practitioners in the MT community. However, previous approaches require either a significant update of the model architecture and/or additional training efforts to make the models well-behaved when TMs are taken as additional input. In this paper, we present a simple but effective method to introduce TMs into neural machine translation (NMT) systems. Specifically, we treat TMs as prompts to the NMT model at test time, but leave the training process unchanged. The result is a slight update of an existing NMT system, which can be implemented in a few hours by anyone who is familiar with NMT. Experimental results on several datasets demonstrate that our system significantly outperforms strong baselines.