@inproceedings{scherrer-etal-2019-university,
    title = "The {U}niversity of {H}elsinki Submissions to the {WMT}19 Similar Language Translation Task",
    author = "Scherrer, Yves  and
      V{\'a}zquez, Ra{\'u}l  and
      Virpioja, Sami",
    editor = "Bojar, Ond{\v{r}}ej  and
      Chatterjee, Rajen  and
      Federmann, Christian  and
      Fishel, Mark  and
      Graham, Yvette  and
      Haddow, Barry  and
      Huck, Matthias  and
      Yepes, Antonio Jimeno  and
      Koehn, Philipp  and
      Martins, Andr{\'e}  and
      Monz, Christof  and
      Negri, Matteo  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Post, Matt  and
      Turchi, Marco  and
      Verspoor, Karin",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/W19-5432/",
    doi = "10.18653/v1/W19-5432",
    pages = "236--244",
    abstract = "This paper describes the University of Helsinki Language Technology group`s participation in the WMT 2019 similar language translation task. We trained neural machine translation models for the language pairs Czech {\ensuremath{<}}-{\ensuremath{>}} Polish and Spanish {\ensuremath{<}}-{\ensuremath{>}} Portuguese. Our experiments focused on different subword segmentation methods, and in particular on the comparison of a cognate-aware segmentation method, Cognate Morfessor, with character segmentation and unsupervised segmentation methods for which the data from different languages were simply concatenated. We did not observe major benefits from cognate-aware segmentation methods, but further research may be needed to explore larger parts of the parameter space. Character-level models proved to be competitive for translation between Spanish and Portuguese, but they are slower in training and decoding."
}