%0 Journal Article
	%A Yifan Fan and  Xudong Luo and  Pingping Lin
	%D 2020
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 168, 2020
	%T A Survey of Response Generation of Dialogue Systems
	%U https://publications.waset.org/pdf/10011629
	%V 168
	%X An essential task in the field of artificial intelligence is
to allow computers to interact with people through natural language.
Therefore, researches such as virtual assistants and dialogue systems
have received widespread attention from industry and academia. The
response generation plays a crucial role in dialogue systems, so to
push forward the research on this topic, this paper surveys various
methods for response generation. We sort out these methods into
three categories. First one includes finite state machine methods,
framework methods, and instance methods. The second contains
full-text indexing methods, ontology methods, vast knowledge base
method, and some other methods. The third covers retrieval methods
and generative methods. We also discuss some hybrid methods based
knowledge and deep learning. We compare their disadvantages and
advantages and point out in which ways these studies can be improved
further. Our discussion covers some studies published in leading
conferences such as IJCAI and AAAI in recent years.
	%P 461 - 472