# Search Engine Guided Non-Parametric Neural Machine Translation

arXiv: https://arxiv.org/abs/1705.07267 · HTML: https://mlsanity.com/paper/1705.07267/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2017.

## Built on by (most cited first, 10 of 10)

- [Efficient Methods for Natural Language Processing: A Survey](https://mlsanity.com/paper/2209.00099/) (2022)
- [A Benchmark for Learning to Translate a New Language from One Grammar Book](https://mlsanity.com/paper/2309.16575/) (2023)
- [Goldfish: Vision-Language Understanding of Arbitrarily Long Videos](https://mlsanity.com/paper/2407.12679/) (2024)
- [Neural Machine Translation with Phrase-Level Universal Visual Representations](https://mlsanity.com/paper/2203.10299/) (2022)
- [Few-Shot Table-to-Text Generation with Prototype Memory](https://mlsanity.com/paper/2108.12516/) (2021)
- [Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions](https://mlsanity.com/paper/2210.05047/) (2022)
- [Investigating Neural Machine Translation for Low-Resource Languages: Using Bavarian as a Case Study](https://mlsanity.com/paper/2404.08259/) (2024)
- [Improving Robustness of Retrieval Augmented Translation via Shuffling of Suggestions](https://mlsanity.com/paper/2210.05059/) (2022)
- [RSpell: Retrieval-augmented Framework for Domain Adaptive Chinese Spelling Check](https://mlsanity.com/paper/2308.08176/) (2023)
- [Action based Network for Conversation Question Reformulation](https://mlsanity.com/paper/2111.14445/) (2021)

## Abstract (arXiv)

In this paper, we extend an attention-based neural machine translation (NMT) model by allowing it to access an entire training set of parallel sentence pairs even after training. The proposed approach consists of two stages. In the first stage--retrieval stage--, an off-the-shelf, black-box search engine is used to retrieve a small subset of sentence pairs from a training set given a source sentence. These pairs are further filtered based on a fuzzy matching score based on edit distance. In the second stage--translation stage--, a novel translation model, called translation memory enhanced NMT (TM-NMT), seamlessly uses both the source sentence and a set of retrieved sentence pairs to perform the translation. Empirical evaluation on three language pairs (En-Fr, En-De, and En-Es) shows that the proposed approach significantly outperforms the baseline approach and the improvement is more significant when more relevant sentence pairs were retrieved.