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        <title>Multilingual Unsupervised Neural Machine Translation with Denoising Adapters</title>
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        <namePart type="given">Ahmet</namePart>
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        <namePart type="given">Alexandre</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Matthias</namePart>
        <namePart type="family">Gallé</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>We consider the problem of multilingual unsupervised machine translation, translating to and from languages that only have monolingual data by using auxiliary parallel language pairs. For this problem the standard procedure so far to leverage the monolingual data is _back-translation_, which is computationally costly and hard to tune. In this paper we propose instead to use _denoising adapters_, adapter layers with a denoising objective, on top of pre-trained mBART-50. In addition to the modularity and flexibility of such an approach we show that the resulting translations are on-par with back-translating as measured by BLEU, and furthermore it allows adding unseen languages incrementally.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.533</identifier>
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%0 Conference Proceedings
%T Multilingual Unsupervised Neural Machine Translation with Denoising Adapters
%A Üstün, Ahmet
%A Berard, Alexandre
%A Besacier, Laurent
%A Gallé, Matthias
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F ustun-etal-2021-multilingual
%X We consider the problem of multilingual unsupervised machine translation, translating to and from languages that only have monolingual data by using auxiliary parallel language pairs. For this problem the standard procedure so far to leverage the monolingual data is _back-translation_, which is computationally costly and hard to tune. In this paper we propose instead to use _denoising adapters_, adapter layers with a denoising objective, on top of pre-trained mBART-50. In addition to the modularity and flexibility of such an approach we show that the resulting translations are on-par with back-translating as measured by BLEU, and furthermore it allows adding unseen languages incrementally.
%R 10.18653/v1/2021.emnlp-main.533
%U https://aclanthology.org/2021.emnlp-main.533
%U https://doi.org/10.18653/v1/2021.emnlp-main.533
%P 6650-6662

Markdown (Informal)

[Multilingual Unsupervised Neural Machine Translation with Denoising Adapters](https://aclanthology.org/2021.emnlp-main.533) (Üstün et al., EMNLP 2021)

• Multilingual Unsupervised Neural Machine Translation with Denoising Adapters (Üstün et al., EMNLP 2021)

ACL

• Ahmet Üstün, Alexandre Berard, Laurent Besacier, and Matthias Gallé. 2021. Multilingual Unsupervised Neural Machine Translation with Denoising Adapters. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6650–6662, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.