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        <title>An Effective Approach to Unsupervised Machine Translation</title>
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        <namePart type="given">Mikel</namePart>
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    <name type="personal">
        <namePart type="given">Eneko</namePart>
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    <abstract>While machine translation has traditionally relied on large amounts of parallel corpora, a recent research line has managed to train both Neural Machine Translation (NMT) and Statistical Machine Translation (SMT) systems using monolingual corpora only. In this paper, we identify and address several deficiencies of existing unsupervised SMT approaches by exploiting subword information, developing a theoretically well founded unsupervised tuning method, and incorporating a joint refinement procedure. Moreover, we use our improved SMT system to initialize a dual NMT model, which is further fine-tuned through on-the-fly back-translation. Together, we obtain large improvements over the previous state-of-the-art in unsupervised machine translation. For instance, we get 22.5 BLEU points in English-to-German WMT 2014, 5.5 points more than the previous best unsupervised system, and 0.5 points more than the (supervised) shared task winner back in 2014.</abstract>
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%0 Conference Proceedings
%T An Effective Approach to Unsupervised Machine Translation
%A Artetxe, Mikel
%A Labaka, Gorka
%A Agirre, Eneko
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F artetxe-etal-2019-effective
%X While machine translation has traditionally relied on large amounts of parallel corpora, a recent research line has managed to train both Neural Machine Translation (NMT) and Statistical Machine Translation (SMT) systems using monolingual corpora only. In this paper, we identify and address several deficiencies of existing unsupervised SMT approaches by exploiting subword information, developing a theoretically well founded unsupervised tuning method, and incorporating a joint refinement procedure. Moreover, we use our improved SMT system to initialize a dual NMT model, which is further fine-tuned through on-the-fly back-translation. Together, we obtain large improvements over the previous state-of-the-art in unsupervised machine translation. For instance, we get 22.5 BLEU points in English-to-German WMT 2014, 5.5 points more than the previous best unsupervised system, and 0.5 points more than the (supervised) shared task winner back in 2014.
%R 10.18653/v1/P19-1019
%U https://aclanthology.org/P19-1019
%U https://doi.org/10.18653/v1/P19-1019
%P 194-203

Markdown (Informal)

[An Effective Approach to Unsupervised Machine Translation](https://aclanthology.org/P19-1019) (Artetxe et al., ACL 2019)

• An Effective Approach to Unsupervised Machine Translation (Artetxe et al., ACL 2019)

ACL

• Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2019. An Effective Approach to Unsupervised Machine Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 194–203, Florence, Italy. Association for Computational Linguistics.