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        <title>Understanding Back-Translation at Scale</title>
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        <namePart type="given">Sergey</namePart>
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        <namePart type="given">Myle</namePart>
        <namePart type="family">Ott</namePart>
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        <namePart type="given">Michael</namePart>
        <namePart type="family">Auli</namePart>
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    <name type="personal">
        <namePart type="given">David</namePart>
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            <namePart type="given">Ellen</namePart>
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            <namePart type="given">Jun’ichi</namePart>
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    <abstract>An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. This work broadens the understanding of back-translation and investigates a number of methods to generate synthetic source sentences. We find that in all but resource poor settings back-translations obtained via sampling or noised beam outputs are most effective. Our analysis shows that sampling or noisy synthetic data gives a much stronger training signal than data generated by beam or greedy search. We also compare how synthetic data compares to genuine bitext and study various domain effects. Finally, we scale to hundreds of millions of monolingual sentences and achieve a new state of the art of 35 BLEU on the WMT’14 English-German test set.</abstract>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Understanding Back-Translation at Scale
%A Edunov, Sergey
%A Ott, Myle
%A Auli, Michael
%A Grangier, David
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F edunov-etal-2018-understanding
%X An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. This work broadens the understanding of back-translation and investigates a number of methods to generate synthetic source sentences. We find that in all but resource poor settings back-translations obtained via sampling or noised beam outputs are most effective. Our analysis shows that sampling or noisy synthetic data gives a much stronger training signal than data generated by beam or greedy search. We also compare how synthetic data compares to genuine bitext and study various domain effects. Finally, we scale to hundreds of millions of monolingual sentences and achieve a new state of the art of 35 BLEU on the WMT’14 English-German test set.
%R 10.18653/v1/D18-1045
%U https://aclanthology.org/D18-1045
%U https://doi.org/10.18653/v1/D18-1045
%P 489-500

Markdown (Informal)

[Understanding Back-Translation at Scale](https://aclanthology.org/D18-1045) (Edunov et al., EMNLP 2018)

• Understanding Back-Translation at Scale (Edunov et al., EMNLP 2018)

ACL

• Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 2018. Understanding Back-Translation at Scale. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 489–500, Brussels, Belgium. Association for Computational Linguistics.