@InProceedings{farajian-EtAl:2017:EACLshort,
  author    = {Farajian, M. Amin  and  Turchi, Marco  and  Negri, Matteo  and  Bertoldi, Nicola  and  Federico, Marcello},
  title     = {Neural vs. Phrase-Based Machine Translation in a Multi-Domain Scenario},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {280--284},
  abstract  = {State-of-the-art neural machine translation (NMT) systems are generally trained
	on specific domains by carefully selecting the training sets and applying
	proper domain adaptation techniques. 
	In this paper we consider the real world scenario in which the target domain is
	not predefined, hence the system should be able to translate text from multiple
	domains. We compare the performance of a generic NMT system and phrase-based
	statistical machine translation (PBMT) system by training them on a generic
	parallel corpus composed of data from different domains.
	Our results on multi-domain English-French data show that, in these realistic
	conditions, PBMT outperforms its neural counterpart. This raises the question:
	is NMT ready for deployment as a generic/multi-purpose MT backbone in
	real-world settings?},
  url       = {http://www.aclweb.org/anthology/E17-2045}
}