@inproceedings{chousa-morishita-2021-input,
    title = "Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: {NTT} at {WAT} 2021",
    author = "Chousa, Katsuki  and
      Morishita, Makoto",
    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/transition-to-people-yaml/2021.wat-1.3/",
    doi = "10.18653/v1/2021.wat-1.3",
    pages = "53--61",
    abstract = "This paper describes our systems that were submitted to the restricted translation task at WAT 2021. In this task, the systems are required to output translated sentences that contain all given word constraints. Our system combined input augmentation and constrained beam search algorithms. Through experiments, we found that this combination significantly improves translation accuracy and can save inference time while containing all the constraints in the output. For both En-{\ensuremath{>}}Ja and Ja-{\ensuremath{>}}En, our systems obtained the best evaluation performances in automatic and human evaluation."
}