Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions

• URL: http://arxiv.org/abs/2210.05047v1
• Date: Mon, 10 Oct 2022 23:33:15 GMT
• Title: Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions
• Authors: Cuong Hoang, Devendra Sachan, Prashant Mathur, Brian Thompson, Marcello Federico
• Abstract summary: We build on the idea of Retrieval Augmented Translation (RAT) where top-k in-domain fuzzy matches are found for the source sentence. We propose a novel architecture to control interactions between a source sentence and the top-k fuzzy target-language matches.
• Score: 15.845071122977158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 target-language 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.

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