@article{(Open Science Index):https://publications.waset.org/pdf/10011653,
	  title     = {On Dialogue Systems Based on Deep Learning},
	  author    = {Yifan Fan and  Xudong Luo and  Pingping Lin},
	  country	= {},
	  institution	= {},
	  abstract     = {Nowadays, dialogue systems increasingly become the
way for humans to access many computer systems. So, humans
can interact with computers in natural language. A dialogue
system consists of three parts: understanding what humans say in
natural language, managing dialogue, and generating responses in
natural language. In this paper, we survey deep learning based
methods for dialogue management, response generation and dialogue
evaluation. Specifically, these methods are based on neural network,
long short-term memory network, deep reinforcement learning,
pre-training and generative adversarial network. We compare these
methods and point out the further research directions.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {14},
	  number    = {12},
	  year      = {2020},
	  pages     = {508 - 516},
	  ee        = {https://publications.waset.org/pdf/10011653},
	  url   	= {https://publications.waset.org/vol/168},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 168, 2020},
	}