# Multilingual k-Nearest-Neighbor Machine Translation

arXiv: https://arxiv.org/abs/2310.14644 · HTML: https://mlsanity.com/paper/2310.14644/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 24 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges](https://mlsanity.com/paper/1907.05019/) (2019)
- [Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2010.00710/) (2020)
- [When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?](https://mlsanity.com/paper/1804.06323/) (2018)
- [Generating Sentences by Editing Prototypes](https://mlsanity.com/paper/1709.08878/) (2017)
- [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)
- [Trivial Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1809.00357/) (2018)
- [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)
- [Towards Robust k-Nearest-Neighbor Machine Translation](https://mlsanity.com/paper/2210.08808/) (2022)
- [Viewing Knowledge Transfer in Multilingual Machine Translation Through a Representational Lens](https://mlsanity.com/paper/2305.11550/) (2023)
- [From Bilingual to Multilingual Neural Machine Translation by Incremental Training](https://mlsanity.com/paper/1907.00735/) (2019)
- [kNN-BOX: A Unified Framework for Nearest Neighbor Generation](https://mlsanity.com/paper/2302.13574/) (2023)

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

- [On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.00597/) (2025)
- [Efficient k-Nearest-Neighbor Machine Translation with Dynamic Retrieval](https://mlsanity.com/paper/2406.06073/) (2024)
- [Analyzing and Improving Cross-lingual Knowledge Transfer for Machine Translation](https://mlsanity.com/paper/2601.04036/) (2026)

## Abstract (arXiv)

k-nearest-neighbor machine translation has demonstrated remarkable improvements in machine translation quality by creating a datastore of cached examples. However, these improvements have been limited to high-resource language pairs, with large datastores, and remain a challenge for low-resource languages. In this paper, we address this issue by combining representations from multiple languages into a single datastore. Our results consistently demonstrate substantial improvements not only in low-resource translation quality (up to +3.6 BLEU), but also for high-resource translation quality (up to +0.5 BLEU). Our experiments show that it is possible to create multilingual datastores that are a quarter of the size, achieving a 5.3x speed improvement, by using linguistic similarities for datastore creation.