Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions
Cuong Hoang, Devendra Singh Sachan, Prashant Mathur, Brian Thompson, Marcello Federico · 2023
We explore zero-shot adaptation, where a general-domain model has access to customer or domain specific parallel data at inference time, but not during training.We build on the idea of Retrieval Augmented Translation (RAT) where top-k in-domain fuzzy matches are found for the source sentence, and targetlanguage translations of those fuzzy-matched sentences are provided to the translation model at inference time.We propose a novel architecture to control interactions between a source sentence and the top-k fuzzy target-language matches, and compare it to architectures from prior work.We conduct experiments in two language pairs (En-De and En-Fr) by training models on WMT data and testing them with five and seven multi-domain datasets, respectively.Our approach consistently outperforms the alternative architectures, improving BLEU across language pair, domain, and number k of fuzzy matches with almost no trade-off on inference latency.