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        <title>Distilling Multiple Domains for Neural Machine Translation</title>
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    <name type="personal">
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    <abstract>Neural machine translation achieves impressive results in high-resource conditions, but performance often suffers when the input domain is low-resource. The standard practice of adapting a separate model for each domain of interest does not scale well in practice from both a quality perspective (brittleness under domain shift) as well as a cost perspective (added maintenance and inference complexity). In this paper, we propose a framework for training a single multi-domain neural machine translation model that is able to translate several domains without increasing inference time or memory usage. We show that this model can improve translation on both high- and low-resource domains over strong multi-domain baselines. In addition, our proposed model is effective when domain labels are unknown during training, as well as robust under noisy data conditions.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.364</identifier>
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%0 Conference Proceedings
%T Distilling Multiple Domains for Neural Machine Translation
%A Currey, Anna
%A Mathur, Prashant
%A Dinu, Georgiana
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F currey-etal-2020-distilling
%X Neural machine translation achieves impressive results in high-resource conditions, but performance often suffers when the input domain is low-resource. The standard practice of adapting a separate model for each domain of interest does not scale well in practice from both a quality perspective (brittleness under domain shift) as well as a cost perspective (added maintenance and inference complexity). In this paper, we propose a framework for training a single multi-domain neural machine translation model that is able to translate several domains without increasing inference time or memory usage. We show that this model can improve translation on both high- and low-resource domains over strong multi-domain baselines. In addition, our proposed model is effective when domain labels are unknown during training, as well as robust under noisy data conditions.
%R 10.18653/v1/2020.emnlp-main.364
%U https://aclanthology.org/2020.emnlp-main.364
%U https://doi.org/10.18653/v1/2020.emnlp-main.364
%P 4500-4511

Markdown (Informal)

[Distilling Multiple Domains for Neural Machine Translation](https://aclanthology.org/2020.emnlp-main.364) (Currey et al., EMNLP 2020)

• Distilling Multiple Domains for Neural Machine Translation (Currey et al., EMNLP 2020)

ACL

• Anna Currey, Prashant Mathur, and Georgiana Dinu. 2020. Distilling Multiple Domains for Neural Machine Translation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4500–4511, Online. Association for Computational Linguistics.