@inproceedings{imankulova-etal-2019-japanese,
title = "{J}apanese-{R}ussian {TMU} Neural Machine Translation System using Multilingual Model for {WAT} 2019",
author = "Imankulova, Aizhan and
Kaneko, Masahiro and
Komachi, Mamoru",
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/D19-5221",
doi = "10.18653/v1/D19-5221",
pages = "165--170",
abstract = "We introduce our system that is submitted to the News Commentary task (Japanese{\textless}-{\textgreater}Russian) of the 6th Workshop on Asian Translation. The goal of this shared task is to study extremely low resource situations for distant language pairs. It is known that using parallel corpora of different language pair as training data is effective for multilingual neural machine translation model in extremely low resource scenarios. Therefore, to improve the translation quality of Japanese{\textless}-{\textgreater}Russian language pair, our method leverages other in-domain Japanese-English and English-Russian parallel corpora as additional training data for our multilingual NMT model.",
}
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<abstract>We introduce our system that is submitted to the News Commentary task (Japanese\textless-\textgreaterRussian) of the 6th Workshop on Asian Translation. The goal of this shared task is to study extremely low resource situations for distant language pairs. It is known that using parallel corpora of different language pair as training data is effective for multilingual neural machine translation model in extremely low resource scenarios. Therefore, to improve the translation quality of Japanese\textless-\textgreaterRussian language pair, our method leverages other in-domain Japanese-English and English-Russian parallel corpora as additional training data for our multilingual NMT model.</abstract>
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%0 Conference Proceedings
%T Japanese-Russian TMU Neural Machine Translation System using Multilingual Model for WAT 2019
%A Imankulova, Aizhan
%A Kaneko, Masahiro
%A Komachi, Mamoru
%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 imankulova-etal-2019-japanese
%X We introduce our system that is submitted to the News Commentary task (Japanese\textless-\textgreaterRussian) of the 6th Workshop on Asian Translation. The goal of this shared task is to study extremely low resource situations for distant language pairs. It is known that using parallel corpora of different language pair as training data is effective for multilingual neural machine translation model in extremely low resource scenarios. Therefore, to improve the translation quality of Japanese\textless-\textgreaterRussian language pair, our method leverages other in-domain Japanese-English and English-Russian parallel corpora as additional training data for our multilingual NMT model.
%R 10.18653/v1/D19-5221
%U https://aclanthology.org/D19-5221
%U https://doi.org/10.18653/v1/D19-5221
%P 165-170
Markdown (Informal)
[Japanese-Russian TMU Neural Machine Translation System using Multilingual Model for WAT 2019](https://aclanthology.org/D19-5221) (Imankulova et al., WAT 2019)
ACL