@InProceedings{zhao-EtAl:2017:W17-55,
  author    = {Zhao, Tiancheng  and  Lu, Allen  and  Lee, Kyusong  and  Eskenazi, Maxine},
  title     = {Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability},
  booktitle = {Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue},
  month     = {August},
  year      = {2017},
  address   = {Saarbrücken, Germany},
  publisher = {Association for Computational Linguistics},
  pages     = {27--36},
  abstract  = {Generative encoder-decoder models offer great promise in developing
	domain-general dialog systems. However, they have mainly been applied to
	open-domain conversations. This paper presents a practical and novel framework
	for building task-oriented dialog systems based on encoder-decoder models. This
	framework enables encoder-decoder models to accomplish slot-value independent
	decision-making and interact with external databases. Moreover, this paper
	shows the flexibility of the proposed method by interleaving chatting
	capability with a slot-filling system for better out-of-domain recovery. The
	models were trained on both real-user data from a bus information system and
	human-human chat data. Results show that the proposed framework achieves good
	performance in both offline evaluation metrics and in task success rate with
	human users.},
  url       = {http://aclweb.org/anthology/W17-5505}
}