@InProceedings{chen-gao:2017:I17-5,
  author    = {Chen, Yun-Nung  and  Gao, Jianfeng},
  title     = {Open-Domain Neural Dialogue Systems},
  booktitle = {Proceedings of the IJCNLP 2017, Tutorial Abstracts},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {6--10},
  abstract  = {In the past decade, spoken dialogue systems have been the most prominent
	component in today's personal assistants.
	A lot of devices have incorporated dialogue system modules, which allow users
	to speak naturally in order to finish tasks more efficiently.
	The traditional conversational systems have rather complex and/or modular
	pipelines.
	The advance of deep learning technologies has recently risen the applications
	of neural models to dialogue modeling.
	Nevertheless, applying deep learning technologies for building robust and
	scalable dialogue systems is still a challenging task and an open research area
	as it requires deeper understanding of the classic pipelines as well as
	detailed knowledge on the benchmark of the models of the prior work and the
	recent state-of-the-art work.
	Therefore, this tutorial is designed to focus on an overview of the dialogue
	system development while  describing most recent research for building
	task-oriented and chit-chat dialogue systems, and summarizing the challenges.
	We target the audience of students and practitioners who have some deep
	learning background, who want to get more familiar with conversational dialogue
	systems.},
  url       = {http://www.aclweb.org/anthology/I17-5003}
}