@inproceedings{marie-etal-2019-supervised,
title = "Supervised and Unsupervised Machine Translation for {M}yanmar-{E}nglish and {K}hmer-{E}nglish",
author = "Marie, Benjamin and
Kaing, Hour and
Mon, Aye Myat and
Ding, Chenchen and
Fujita, Atsushi and
Utiyama, Masao and
Sumita, Eiichiro",
editor = "Nakazawa, Toshiaki and
Ding, Chenchen and
Dabre, Raj and
Kunchukuttan, Anoop and
Doi, Nobushige and
Oda, Yusuke and
Bojar, Ond{\v{r}}ej and
Parida, Shantipriya and
Goto, Isao and
Mino, Hidaya",
booktitle = "Proceedings of the 6th Workshop on Asian Translation",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5206",
doi = "10.18653/v1/D19-5206",
pages = "68--75",
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.",
}
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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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%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
%Y Nakazawa, Toshiaki
%Y Ding, Chenchen
%Y Dabre, Raj
%Y Kunchukuttan, Anoop
%Y Doi, Nobushige
%Y Oda, Yusuke
%Y Bojar, Ondřej
%Y Parida, Shantipriya
%Y Goto, Isao
%Y Mino, Hidaya
%S Proceedings of the 6th Workshop on Asian Translation
%D 2019
%8 November
%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., WAT 2019)
ACL