@InProceedings{shen-EtAl:2017:Short,
  author    = {Shen, Xiaoyu  and  Su, Hui  and  Li, Yanran  and  Li, Wenjie  and  Niu, Shuzi  and  Zhao, Yang  and  Aizawa, Akiko  and  Long, Guoping},
  title     = {A Conditional Variational Framework for Dialog Generation},
  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     = {504--509},
  abstract  = {Deep latent variable models have been shown to facilitate the response
	generation for open-domain dialog systems. However, these latent variables are
	highly randomized, leading to uncontrollable generated responses. In this
	paper, we propose a framework allowing conditional response generation based on
	specific attributes. These attributes can be either manually assigned or
	automatically detected. Moreover, the dialog states for both speakers are
	modeled separately in order to reflect personal features. We validate this
	framework on two different scenarios, where the attribute refers to genericness
	and sentiment states respectively. The experiment result testified the
	potential of our model, where meaningful responses can be generated in
	accordance with the specified attributes.},
  url       = {http://aclweb.org/anthology/P17-2080}
}