Learning to balance the coherence and diversity of response generation in generation-based chatbots

doi:10.1177/1729881420953006

	Learning to balance the coherence and diversity of response generation in generation-based chatbots
	Wang, Shuliang 1,2; Li, Dapeng 1; Geng, Jing 1,2; Yang, Longxing 3; Leng, Hongyong 1
	2020-07-01
发表期刊	INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS
ISSN	1729-8814
卷号	17 期号:4 页码:11
摘要	Generating response with both coherence and diversity is a challenging task in generation-based chatbots. It is more difficult to improve the coherence and diversity of dialog generation at the same time in the response generation model. In this article, we propose an improved method that improves the coherence and diversity of dialog generation by changing the model to use gamma sampling and adding attention mechanism to the knowledge-guided conditional variational autoencoder. The experimental results demonstrate that our proposed method can significantly improve the coherence and diversity of knowledge-guided conditional variational autoencoder for response generation in generation-based chatbots at the same time.
关键词	Variational autoencoder dialog system deep learning response generation chatbots
DOI	10.1177/1729881420953006
收录类别	SCI
语种	英语
资助项目	Beijing Municipal Science and Technology Project[Z171100005117002] ; Open Fund of Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoformation[2017NGCMZD03]
WOS研究方向	Robotics
WOS类目	Robotics
WOS记录号	WOS:000567168500001
出版者	SAGE PUBLICATIONS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/15503
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Dapeng; Geng, Jing
作者单位	1.Beijing Inst Technol, Sch Comp Sci & Technol, 5 South St, Beijing 100081, Peoples R China 2.Beijing Inst Technol, Acad E Govt, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Wang, Shuliang,Li, Dapeng,Geng, Jing,et al. Learning to balance the coherence and diversity of response generation in generation-based chatbots[J]. INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS,2020,17(4):11.
APA	Wang, Shuliang,Li, Dapeng,Geng, Jing,Yang, Longxing,&Leng, Hongyong.(2020).Learning to balance the coherence and diversity of response generation in generation-based chatbots.INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS,17(4),11.
MLA	Wang, Shuliang,et al."Learning to balance the coherence and diversity of response generation in generation-based chatbots".INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 17.4(2020):11.