# Efficient Cluster-Based k-Nearest-Neighbor Machine Translation

arXiv: https://arxiv.org/abs/2204.06175 · HTML: https://mlsanity.com/paper/2204.06175/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia](https://mlsanity.com/paper/1907.05791/) (2019)
- [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)
- [Efficient Nearest Neighbor Language Models](https://mlsanity.com/paper/2109.04212/) (2021)
- [Adaptive Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2105.13022/) (2021)
- [Learning Kernel-Smoothed Machine Translation with Retrieved Examples](https://mlsanity.com/paper/2109.09991/) (2021)
- [Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation](https://mlsanity.com/paper/2109.06604/) (2021)

## Built on by (most cited first, 24 of 24)

- [Why do Nearest Neighbor Language Models Work?](https://mlsanity.com/paper/2301.02828/) (2023)
- [Towards Robust k-Nearest-Neighbor Machine Translation](https://mlsanity.com/paper/2210.08808/) (2022)
- [Simple and Scalable Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2302.12188/) (2023)
- [What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation](https://mlsanity.com/paper/2211.04052/) (2022)
- [INK: Injecting kNN Knowledge in Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2306.06381/) (2023)
- [NNOSE: Nearest Neighbor Occupational Skill Extraction](https://mlsanity.com/paper/2401.17092/) (2024)
- [Learning Decoupled Retrieval Representation for Nearest Neighbour Neural Machine Translation](https://mlsanity.com/paper/2209.08738/) (2022)
- [kNN-BOX: A Unified Framework for Nearest Neighbor Generation](https://mlsanity.com/paper/2302.13574/) (2023)
- [Better Datastore, Better Translation: Generating Datastores from Pre-Trained Models for Nearest Neural Machine Translation](https://mlsanity.com/paper/2212.08822/) (2022)
- [Generating Diverse Translation with Perturbed kNN-MT](https://mlsanity.com/paper/2402.09344/) (2024)
- [Efficient k-Nearest-Neighbor Machine Translation with Dynamic Retrieval](https://mlsanity.com/paper/2406.06073/) (2024)
- [N-Gram Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2301.12866/) (2023)
- [Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer](https://mlsanity.com/paper/2305.13034/) (2023)
- [Rethinking Translation Memory Augmented Neural Machine Translation](https://mlsanity.com/paper/2306.06948/) (2023)
- [knn-seq: Efficient, Extensible kNN-MT Framework](https://mlsanity.com/paper/2310.12352/) (2023)
- [Towards Faster k-Nearest-Neighbor Machine Translation](https://mlsanity.com/paper/2312.07419/) (2023)
- [Angular Dispersion Accelerates k-Nearest Neighbors Machine Translation](https://mlsanity.com/paper/2509.16729/) (2025)
- [Blur the Linguistic Boundary: Interpreting Chinese Buddhist Sutra in English via Neural Machine Translation](https://mlsanity.com/paper/2209.15164/) (2022)
- [Non-parametric, Nearest-neighbor-assisted Fine-tuning for Neural Machine Translation](https://mlsanity.com/paper/2305.13648/) (2023)
- [Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation](https://mlsanity.com/paper/2305.16599/) (2023)
- [Simply Trainable Nearest Neighbour Machine Translation with GPU Inference](https://mlsanity.com/paper/2407.19965/) (2024)
- [A Debiased Nearest Neighbors Framework for Multi-Label Text Classification](https://mlsanity.com/paper/2408.03202/) (2024)
- [Psychology-Driven Enhancement of Humour Translation](https://mlsanity.com/paper/2507.09259/) (2025)
- [Worlds Within Words: Translating Culture in Ancient Chinese Texts with Multi-Agent Coordination](https://mlsanity.com/paper/2606.01276/) (2026)

## Abstract (arXiv)

k-Nearest-Neighbor Machine Translation (kNN-MT) has been recently proposed as a non-parametric solution for domain adaptation in neural machine translation (NMT). It aims to alleviate the performance degradation of advanced MT systems in translating out-of-domain sentences by coordinating with an additional token-level feature-based retrieval module constructed from in-domain data. Previous studies have already demonstrated that non-parametric NMT is even superior to models fine-tuned on out-of-domain data. In spite of this success, kNN retrieval is at the expense of high latency, in particular for large datastores. To make it practical, in this paper, we explore a more efficient kNN-MT and propose to use clustering to improve the retrieval efficiency. Concretely, we first propose a cluster-based Compact Network for feature reduction in a contrastive learning manner to compress context features into 90+% lower dimensional vectors. We then suggest a cluster-based pruning solution to filter out 10%-40% redundant nodes in large datastores while retaining translation quality. Our proposed methods achieve better or comparable performance while reducing up to 57% inference latency against the advanced non-parametric MT model on several machine translation benchmarks. Experimental results indicate that the proposed methods maintain the most useful information of the original datastore and the Compact Network shows good generalization on unseen domains.