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    <titleInfo>
        <title>Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English</title>
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    <name type="personal">
        <namePart type="given">Benjamin</namePart>
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    <name type="personal">
        <namePart type="given">Aye</namePart>
        <namePart type="given">Myat</namePart>
        <namePart type="family">Mon</namePart>
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    <name type="personal">
        <namePart type="given">Chenchen</namePart>
        <namePart type="family">Ding</namePart>
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    <name type="personal">
        <namePart type="given">Atsushi</namePart>
        <namePart type="family">Fujita</namePart>
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    <name type="personal">
        <namePart type="given">Masao</namePart>
        <namePart type="family">Utiyama</namePart>
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    <name type="personal">
        <namePart type="given">Eiichiro</namePart>
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            <title>Proceedings of the 6th Workshop on Asian Translation</title>
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    <abstract>This paper presents the NICT’s supervised and unsupervised machine translation systems for the WAT2019 Myanmar-English and Khmer-English translation tasks. For all the translation directions, we built state-of-the-art supervised neural (NMT) and statistical (SMT) machine translation systems, using monolingual data cleaned and normalized. Our combination of NMT and SMT performed among the best systems for the four translation directions. We also investigated the feasibility of unsupervised machine translation for low-resource and distant language pairs and confirmed observations of previous work showing that unsupervised MT is still largely unable to deal with them.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5206</identifier>
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%0 Conference Proceedings
%T Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English
%A Marie, Benjamin
%A Kaing, Hour
%A Mon, Aye Myat
%A Ding, Chenchen
%A Fujita, Atsushi
%A Utiyama, Masao
%A Sumita, Eiichiro
%S Proceedings of the 6th Workshop on Asian Translation
%D 2019
%8 nov
%I Association for Computational Linguistics
%C Hong Kong, China
%F marie-etal-2019-supervised
%X This paper presents the NICT’s supervised and unsupervised machine translation systems for the WAT2019 Myanmar-English and Khmer-English translation tasks. For all the translation directions, we built state-of-the-art supervised neural (NMT) and statistical (SMT) machine translation systems, using monolingual data cleaned and normalized. Our combination of NMT and SMT performed among the best systems for the four translation directions. We also investigated the feasibility of unsupervised machine translation for low-resource and distant language pairs and confirmed observations of previous work showing that unsupervised MT is still largely unable to deal with them.
%R 10.18653/v1/D19-5206
%U https://aclanthology.org/D19-5206
%U https://doi.org/10.18653/v1/D19-5206
%P 68-75

Markdown (Informal)

[Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English](https://aclanthology.org/D19-5206) (Marie et al., EMNLP 2019)

• Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English (Marie et al., EMNLP 2019)

ACL

• Benjamin Marie, Hour Kaing, Aye Myat Mon, Chenchen Ding, Atsushi Fujita, Masao Utiyama, and Eiichiro Sumita. 2019. Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English. In Proceedings of the 6th Workshop on Asian Translation, pages 68–75, Hong Kong, China. Association for Computational Linguistics.