@inproceedings{hoang-etal-2023-improving,
title = "Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions",
author = "Hoang, Cuong and
Sachan, Devendra and
Mathur, Prashant and
Thompson, Brian and
Federico, Marcello",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.22",
doi = "10.18653/v1/2023.findings-eacl.22",
pages = "289--295",
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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<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.</abstract>
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%0 Conference Proceedings
%T Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions
%A Hoang, Cuong
%A Sachan, Devendra
%A Mathur, Prashant
%A Thompson, Brian
%A Federico, Marcello
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F hoang-etal-2023-improving
%X 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.
%R 10.18653/v1/2023.findings-eacl.22
%U https://aclanthology.org/2023.findings-eacl.22
%U https://doi.org/10.18653/v1/2023.findings-eacl.22
%P 289-295
Markdown (Informal)
[Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions](https://aclanthology.org/2023.findings-eacl.22) (Hoang et al., Findings 2023)
ACL