@inproceedings{tars-fishel-2018-multi,
    title = "Multi-Domain Neural Machine Translation",
    author = "Tars, Sander  and
      Fishel, Mark",
    editor = "P{\'e}rez-Ortiz, Juan Antonio  and
      S{\'a}nchez-Mart{\'i}nez, Felipe  and
      Espl{\`a}-Gomis, Miquel  and
      Popovi{\'c}, Maja  and
      Rico, Celia  and
      Martins, Andr{\'e}  and
      Van den Bogaert, Joachim  and
      Forcada, Mikel L.",
    booktitle = "Proceedings of the 21st Annual Conference of the European Association for Machine Translation",
    month = may,
    year = "2018",
    address = "Alicante, Spain",
    url = "https://preview.aclanthology.org/ingest-emnlp/2018.eamt-main.26/",
    pages = "279--288",
    abstract = "We present an approach to neural machine translation (NMT) that supports multiple domains in a single model and allows switching between the domains when translating. The core idea is to treat text domainsasdistinctlanguagesandusemultilingual NMT methods to create multi-domain translation systems; we show that this approach results in significant translation quality gains over fine-tuning. We also explore whether the knowledge of pre-specified text domains is necessary; turns out that it is after all, but also that when it is not known quite high translation quality can be reached, and even higher than with known domains in some cases."
}