@inproceedings{imamura-sumita-2020-transformer,
    title = "Transformer-based Double-token Bidirectional Autoregressive Decoding in Neural Machine Translation",
    author = "Imamura, Kenji  and
      Sumita, Eiichiro",
    editor = "Nakazawa, Toshiaki  and
      Nakayama, Hideki  and
      Ding, Chenchen  and
      Dabre, Raj  and
      Kunchukuttan, Anoop  and
      Pa, Win Pa  and
      Bojar, Ond{\v{r}}ej  and
      Parida, Shantipriya  and
      Goto, Isao  and
      Mino, Hidaya  and
      Manabe, Hiroshi  and
      Sudoh, Katsuhito  and
      Kurohashi, Sadao  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.wat-1.3/",
    doi = "10.18653/v1/2020.wat-1.3",
    pages = "50--57",
    abstract = "This paper presents a simple method that extends a standard Transformer-based autoregressive decoder, to speed up decoding. The proposed method generates a token from the head and tail of a sentence (two tokens in total) in each step. By simultaneously generating multiple tokens that rarely depend on each other, the decoding speed is increased while the degradation in translation quality is minimized. In our experiments, the proposed method increased the translation speed by around 113{\%}-155{\%} in comparison with a standard autoregressive decoder, while degrading the BLEU scores by no more than 1.03. It was faster than an iterative non-autoregressive decoder in many conditions."
}