@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://preview.aclanthology.org/iwcs-25-ingestion/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{\ensuremath{<}}-{\ensuremath{>}}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{\ensuremath{<}}-{\ensuremath{>}}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."
}