@InProceedings{kobus-crego-senellart:2017:RANLP,
  author    = {KOBUS, Catherine  and  Crego, Josep  and  Senellart, Jean},
  title     = {Domain Control for Neural Machine Translation},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {372--378},
  abstract  = {Machine translation systems are very sensitive to the domains they were trained
	on. Several domain adaptation techniques have already been deeply studied. We
	propose a
	new technique for neural machine translation (NMT) that we call domain control
	which is performed at runtime using a unique neural network covering multiple
	domains. The presented approach shows quality improvements when compared to
	dedicated domains translating on any of the covered domains and even on
	out-of-domain data. In addition, model parameters do not need to be
	re-estimated for each domain, making this effective to real use cases.
	Evaluation is carried out on English-to-French translation for two different
	testing scenarios. We first consider the case where an end-user performs
	translations on a known domain. Secondly, we consider the scenario where the
	domain is not known and predicted at the sentence level before translating.
	Results show consistent accuracy improvements for both conditions.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_049}
}