@InProceedings{chu-dabre-kurohashi:2017:Short,
  author    = {Chu, Chenhui  and  Dabre, Raj  and  Kurohashi, Sadao},
  title     = {An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {385--391},
  abstract  = {In this paper, we propose a novel domain adaptation method named "mixed fine
	tuning'' 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.},
  url       = {http://aclweb.org/anthology/P17-2061}
}