@inproceedings{kim-komachi-2021-tmu,
    title = "{TMU} {NMT} System with {J}apanese {BART} for the Patent task of {WAT} 2021",
    author = "Kim, Hwichan  and
      Komachi, Mamoru",
    editor = "Nakazawa, Toshiaki  and
      Nakayama, Hideki  and
      Goto, Isao  and
      Mino, Hideya  and
      Ding, Chenchen  and
      Dabre, Raj  and
      Kunchukuttan, Anoop  and
      Higashiyama, Shohei  and
      Manabe, Hiroshi  and
      Pa, Win Pa  and
      Parida, Shantipriya  and
      Bojar, Ond{\v{r}}ej  and
      Chu, Chenhui  and
      Eriguchi, Akiko  and
      Abe, Kaori  and
      Oda, Yusuke  and
      Sudoh, Katsuhito  and
      Kurohashi, Sadao  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 8th Workshop on Asian Translation (WAT2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.wat-1.13/",
    doi = "10.18653/v1/2021.wat-1.13",
    pages = "133--137",
    abstract = "In this paper, we introduce our TMU Neural Machine Translation (NMT) system submitted for the Patent task (Korean Japanese and English Japanese) of 8th Workshop on Asian Translation (Nakazawa et al., 2021). Recently, several studies proposed pre-trained encoder-decoder models using monolingual data. One of the pre-trained models, BART (Lewis et al., 2020), was shown to improve translation accuracy via fine-tuning with bilingual data. However, they experimented only Romanian!English translation using English BART. In this paper, we examine the effectiveness of Japanese BART using Japan Patent Office Corpus 2.0. Our experiments indicate that Japanese BART can also improve translation accuracy in both Korean Japanese and English Japanese translations."
}