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        <title>Bi-Directional Neural Machine Translation with Synthetic Parallel Data</title>
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        <namePart type="given">Xing</namePart>
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            <title>Proceedings of the 2nd Workshop on Neural Machine Translation and Generation</title>
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            <namePart type="given">Alexandra</namePart>
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            <namePart type="given">Thang</namePart>
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            <namePart type="given">Yusuke</namePart>
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    <abstract>Despite impressive progress in high-resource settings, Neural Machine Translation (NMT) still struggles in low-resource and out-of-domain scenarios, often failing to match the quality of phrase-based translation. We propose a novel technique that combines back-translation and multilingual NMT to improve performance in these difficult cases. Our technique trains a single model for both directions of a language pair, allowing us to back-translate source or target monolingual data without requiring an auxiliary model. We then continue training on the augmented parallel data, enabling a cycle of improvement for a single model that can incorporate any source, target, or parallel data to improve both translation directions. As a byproduct, these models can reduce training and deployment costs significantly compared to uni-directional models. Extensive experiments show that our technique outperforms standard back-translation in low-resource scenarios, improves quality on cross-domain tasks, and effectively reduces costs across the board.</abstract>
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%0 Conference Proceedings
%T Bi-Directional Neural Machine Translation with Synthetic Parallel Data
%A Niu, Xing
%A Denkowski, Michael
%A Carpuat, Marine
%Y Birch, Alexandra
%Y Finch, Andrew
%Y Luong, Thang
%Y Neubig, Graham
%Y Oda, Yusuke
%S Proceedings of the 2nd Workshop on Neural Machine Translation and Generation
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F niu-etal-2018-bi
%X Despite impressive progress in high-resource settings, Neural Machine Translation (NMT) still struggles in low-resource and out-of-domain scenarios, often failing to match the quality of phrase-based translation. We propose a novel technique that combines back-translation and multilingual NMT to improve performance in these difficult cases. Our technique trains a single model for both directions of a language pair, allowing us to back-translate source or target monolingual data without requiring an auxiliary model. We then continue training on the augmented parallel data, enabling a cycle of improvement for a single model that can incorporate any source, target, or parallel data to improve both translation directions. As a byproduct, these models can reduce training and deployment costs significantly compared to uni-directional models. Extensive experiments show that our technique outperforms standard back-translation in low-resource scenarios, improves quality on cross-domain tasks, and effectively reduces costs across the board.
%R 10.18653/v1/W18-2710
%U https://aclanthology.org/W18-2710
%U https://doi.org/10.18653/v1/W18-2710
%P 84-91

Markdown (Informal)

[Bi-Directional Neural Machine Translation with Synthetic Parallel Data](https://aclanthology.org/W18-2710) (Niu et al., NGT 2018)

• Bi-Directional Neural Machine Translation with Synthetic Parallel Data (Niu et al., NGT 2018)

ACL

• Xing Niu, Michael Denkowski, and Marine Carpuat. 2018. Bi-Directional Neural Machine Translation with Synthetic Parallel Data. In Proceedings of the 2nd Workshop on Neural Machine Translation and Generation, pages 84–91, Melbourne, Australia. Association for Computational Linguistics.