Intention Understanding Model Inspired by CBC Loops

被引:0
作者
Shen, Jiahui [1 ,2 ]
Xiang, Ji [2 ]
Zha, Daren [2 ]
Fu, Tianshu [2 ]
Duan, Dingyang [2 ]
机构
[1] Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China
[2] Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China
来源
PROCEEDINGS OF THE 2019 IEEE 23RD INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD) | 2019年
基金
中国国家自然科学基金;
关键词
Cortico-Basal Ganglia-Cerebellar loops; intention understanding; semantic feature extraction; sentence embedding; NEURAL-NETWORK; BASAL GANGLIA; MEMORY; CEREBELLUM;
D O I
10.1109/cscwd.2019.8791921
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Accurate intention understanding of the user inputs is the key to human-computer interaction (HCI). At present, more and more studies just focus on the improvement of algorithm efficiency and ignore the nature exploration of intention understanding. In humans, working memory is regarded as a cognitive system for handling a range of neuro-cognitive tasks. Because the intention understanding is a kind of human cognitive ability, in this paper we will explore the human cognitive execution mechanism and try to apply it to improve the machines' intention understanding level. First, we demonstrated a cognitive learning model called Cortico-Basal ganglia-Cerebella (CBC) loops plays an important role in the process of working memory. Then, based on the full understanding of the loops operation mechanism, we put forward a new model of intension understanding. Finally, we applied this model on speech data and compared it with other two methods. The results showed that the new model could help to get task-specific vectors and offer further gains in performance on intention understanding.
引用
收藏
页码:105 / 110
页数:6
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