@inproceedings{zhao-etal-2021-tmeku,
    title = "{TMEKU} System for the {WAT}2021 Multimodal Translation Task",
    author = "Zhao, Yuting  and
      Komachi, Mamoru  and
      Kajiwara, Tomoyuki  and
      Chu, Chenhui",
    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/Add-Cong-Liu-Florida-Atlantic-University-author-id/2021.wat-1.20/",
    doi = "10.18653/v1/2021.wat-1.20",
    pages = "174--180",
    abstract = "We introduce our TMEKU system submitted to the English-Japanese Multimodal Translation Task for WAT 2021. We participated in the Flickr30kEnt-JP task and Ambiguous MSCOCO Multimodal task under the constrained condition using only the officially provided datasets. Our proposed system employs soft alignment of word-region for multimodal neural machine translation (MNMT). The experimental results evaluated on the BLEU metric provided by the WAT 2021 evaluation site show that the TMEKU system has achieved the best performance among all the participated systems. Further analysis of the case study demonstrates that leveraging word-region alignment between the textual and visual modalities is the key to performance enhancement in our TMEKU system, which leads to better visual information use."
}