@inproceedings{vazquez-etal-2020-university,
    title = "The {U}niversity of {H}elsinki Submission to the {IWSLT}2020 Offline {S}peech{T}ranslation Task",
    author = {V{\'a}zquez, Ra{\'u}l  and
      Aulamo, Mikko  and
      Sulubacak, Umut  and
      Tiedemann, J{\"o}rg},
    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://preview.aclanthology.org/add-emnlp-2024-awards/2020.iwslt-1.10/",
    doi = "10.18653/v1/2020.iwslt-1.10",
    pages = "95--102",
    abstract = "This paper describes the University of Helsinki Language Technology group`s participation in the IWSLT 2020 offline speech translation task, addressing the translation of English audio into German text. In line with this year`s task objective, we train both cascade and end-to-end systems for spoken language translation. We opt for an end-to-end multitasking architecture with shared internal representations and a cascade approach that follows a standard procedure consisting of ASR, correction, and MT stages. We also describe the experiments that served as a basis for the submitted systems. Our experiments reveal that multitasking training with shared internal representations is not only possible but allows for knowledge-transfer across modalities."
}