@inproceedings{xu-etal-2019-systran,
title = "{SYSTRAN} @ {WAT} 2019: {R}ussian-{J}apanese News Commentary task",
author = "Xu, Jitao and
Nguyen, TuAnh and
Pham, MinhQuang and
Crego, Josep and
Senellart, Jean",
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/D19-5225",
doi = "10.18653/v1/D19-5225",
pages = "189--194",
abstract = "This paper describes Systran{'}s submissions to WAT 2019 Russian-Japanese News Commentary task. A challenging translation task due to the extremely low resources available and the distance of the language pair. We have used the neural Transformer architecture learned over the provided resources and we carried out synthetic data generation experiments which aim at alleviating the data scarcity problem. Results indicate the suitability of the data augmentation experiments, enabling our systems to rank first according to automatic evaluations.",
}
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<title>Proceedings of the 6th Workshop on Asian Translation</title>
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<abstract>This paper describes Systran’s submissions to WAT 2019 Russian-Japanese News Commentary task. A challenging translation task due to the extremely low resources available and the distance of the language pair. We have used the neural Transformer architecture learned over the provided resources and we carried out synthetic data generation experiments which aim at alleviating the data scarcity problem. Results indicate the suitability of the data augmentation experiments, enabling our systems to rank first according to automatic evaluations.</abstract>
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%0 Conference Proceedings
%T SYSTRAN @ WAT 2019: Russian-Japanese News Commentary task
%A Xu, Jitao
%A Nguyen, TuAnh
%A Pham, MinhQuang
%A Crego, Josep
%A Senellart, Jean
%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 xu-etal-2019-systran
%X This paper describes Systran’s submissions to WAT 2019 Russian-Japanese News Commentary task. A challenging translation task due to the extremely low resources available and the distance of the language pair. We have used the neural Transformer architecture learned over the provided resources and we carried out synthetic data generation experiments which aim at alleviating the data scarcity problem. Results indicate the suitability of the data augmentation experiments, enabling our systems to rank first according to automatic evaluations.
%R 10.18653/v1/D19-5225
%U https://aclanthology.org/D19-5225
%U https://doi.org/10.18653/v1/D19-5225
%P 189-194
Markdown (Informal)
[SYSTRAN @ WAT 2019: Russian-Japanese News Commentary task](https://aclanthology.org/D19-5225) (Xu et al., WAT 2019)
ACL