@inproceedings{chu-etal-2017-empirical,
    title = "An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation",
    author = "Chu, Chenhui  and
      Dabre, Raj  and
      Kurohashi, Sadao",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/P17-2061/",
    doi = "10.18653/v1/P17-2061",
    pages = "385--391",
    abstract = "In this paper, we propose a novel domain adaptation method named {\textquotedblleft}mixed fine tuning{\textquotedblright} for neural machine translation (NMT). We combine two existing approaches namely fine tuning and multi domain NMT. We first train an NMT model on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus which is a mix of the in-domain and out-of-domain corpora. All corpora are augmented with artificial tags to indicate specific domains. We empirically compare our proposed method against fine tuning and multi domain methods and discuss its benefits and shortcomings."
}