@inproceedings{elbayad-etal-2020-trac,
    title = "{ON}-{TRAC} Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at {IWSLT} 2020",
    author = "Elbayad, Maha  and
      Nguyen, Ha  and
      Bougares, Fethi  and
      Tomashenko, Natalia  and
      Caubri{\`e}re, Antoine  and
      Lecouteux, Benjamin  and
      Est{\`e}ve, Yannick  and
      Besacier, Laurent",
    editor = {Federico, Marcello  and
      Waibel, Alex  and
      Knight, Kevin  and
      Nakamura, Satoshi  and
      Ney, Hermann  and
      Niehues, Jan  and
      St{\"u}ker, Sebastian  and
      Wu, Dekai  and
      Mariani, Joseph  and
      Yvon, Francois},
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.iwslt-1.2",
    doi = "10.18653/v1/2020.iwslt-1.2",
    pages = "35--43",
    abstract = "This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2020, offline speech translation and simultaneous speech translation. ON-TRAC Consortium is composed of researchers from three French academic laboratories: LIA (Avignon Universit{\'e}), LIG (Universit{\'e} Grenoble Alpes), and LIUM (Le Mans Universit{\'e}). Attention-based encoder-decoder models, trained end-to-end, were used for our submissions to the offline speech translation track. Our contributions focused on data augmentation and ensembling of multiple models. In the simultaneous speech translation track, we build on Transformer-based wait-k models for the text-to-text subtask. For speech-to-text simultaneous translation, we attach a wait-k MT system to a hybrid ASR system. We propose an algorithm to control the latency of the ASR+MT cascade and achieve a good latency-quality trade-off on both subtasks.",
}