@inproceedings{garcia-martinez-etal-2021-neural,
    title = "Neural Translation for {E}uropean {U}nion ({NTEU})",
    author = "Garc{\'\i}a-Mart{\'\i}nez, Mercedes  and
      Bi{\'e}, Laurent  and
      Cerd{\`a}, Aleix  and
      Estela, Amando  and
      Herranz, Manuel  and
      Kri{\v{s}}lauks, Rihards  and
      Melero, Maite  and
      O{'}Dowd, Tony  and
      O{'}Gorman, Sinead  and
      Pinnis, Marcis  and
      Stafanovi{\v{c}}, Art{\=u}rs  and
      Superbo, Riccardo  and
      Vasi{\c{l}}evskis, Art{\=u}rs",
    editor = "Campbell, Janice  and
      Huyck, Ben  and
      Larocca, Stephen  and
      Marciano, Jay  and
      Savenkov, Konstantin  and
      Yanishevsky, Alex",
    booktitle = "Proceedings of Machine Translation Summit XVIII: Users and Providers Track",
    month = aug,
    year = "2021",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://aclanthology.org/2021.mtsummit-up.23",
    pages = "316--334",
    abstract = "The Neural Translation for the European Union (NTEU) engine farm enables direct machine translation for all 24 official languages of the European Union without the necessity to use a high-resourced language as a pivot. This amounts to a total of 552 translation engines for all combinations of the 24 languages. We have collected parallel data for all the language combinations publickly shared in elrc-share.eu. The translation engines have been customized to domain,for the use of the European public administrations. The delivered engines will be published in the European Language Grid. In addition to the usual automatic metrics, all the engines have been evaluated by humans based on the direct assessment methodology. For this purpose, we built an open-source platform called MTET The evaluation shows that most of the engines reach high quality and get better scores compared to an external machine translation service in a blind evaluation setup.",
}