@InProceedings{akama-EtAl:2017:I17-2,
  author    = {Akama, Reina  and  Inada, Kazuaki  and  Inoue, Naoya  and  Kobayashi, Sosuke  and  Inui, Kentaro},
  title     = {Generating Stylistically Consistent Dialog Responses with Transfer Learning},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {408--412},
  abstract  = {We propose a novel, data-driven, and stylistically consistent dialog response
	generation system. 
	To create a user-friendly system, it is crucial to make generated responses not
	only appropriate but also stylistically consistent. For leaning both the
	properties effectively, our proposed framework has two training stages inspired
	by transfer learning. 
	First, we train the model to generate appropriate responses, and then we ensure
	that the responses have a specific style. 
	Experimental results demonstrate that the proposed method produces
	stylistically consistent responses while maintaining the appropriateness of the
	responses learned in a general domain.},
  url       = {http://www.aclweb.org/anthology/I17-2069}
}