Guiding Neural Machine Translation with Retrieved Translation Pieces

TL;DR

This paper introduces a retrieval-augmented method for neural machine translation that enhances low-frequency word translation by incorporating retrieved translation pieces, leading to significant BLEU score improvements with minimal speed impact.

Contribution

The paper presents a novel retrieval-based approach that integrates translation pieces into NMT decoding, improving low-frequency word translation and overall accuracy.

Findings

Up to 6 BLEU point improvements on domain-specific tasks

Method is fast and easy to implement

Outperforms other retrieval-based methods in accuracy and speed

Abstract

One of the difficulties of neural machine translation (NMT) is the recall and appropriate translation of low-frequency words or phrases. In this paper, we propose a simple, fast, and effective method for recalling previously seen translation examples and incorporating them into the NMT decoding process. Specifically, for an input sentence, we use a search engine to retrieve sentence pairs whose source sides are similar with the input sentence, and then collect $n$-grams that are both in the retrieved target sentences and aligned with words that match in the source sentences, which we call "translation pieces". We compute pseudo-probabilities for each retrieved sentence based on similarities between the input sentence and the retrieved source sentences, and use these to weight the retrieved translation pieces. Finally, an existing NMT model is used to translate the input sentence, with an additional bonus given to outputs that contain the collected translation pieces. We show our method improves NMT translation results up to 6 BLEU points on three narrow domain translation tasks where repetitiveness of the target sentences is particularly salient. It also causes little increase in the translation time, and compares favorably to another alternative retrieval-based method with respect to accuracy, speed, and simplicity of implementation.