@inproceedings{grozea-2018-ensemble,
    title = "Ensemble of Translators with Automatic Selection of the Best Translation {--} the submission of {FOKUS} to the {WMT} 18 biomedical translation task {--}",
    author = "Grozea, Cristian",
    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
      Monz, Christof  and
      Negri, Matteo  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Post, Matt  and
      Specia, Lucia  and
      Turchi, Marco  and
      Verspoor, Karin",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W18-6445/",
    doi = "10.18653/v1/W18-6445",
    pages = "644--647",
    abstract = "This paper describes the system of Fraunhofer FOKUS for the WMT 2018 biomedical translation task. Our approach, described here, was to automatically select the most promising translation from a set of candidates produced with NMT (Transformer) models. We selected the highest fidelity translation of each sentence by using a dictionary, stemming and a set of heuristics. Our method is simple, can use any machine translators, and requires no further training in addition to that already employed to build the NMT models. The downside is that the score did not increase over the best in ensemble, but was quite close to it (difference about 0.5 BLEU)."
}