Long Short-Term Memory Networks for Automatic Generation of Conversations

被引:0
作者
Fujita, Tomohiro [1 ]
Bai, Wenjun [1 ]
Quan, Changqin [1 ]
机构
[1] Kobe Univ, Grad Sch Syst Informat, Dept Computat Sci, Kobe, Hyogo, Japan
来源
2017 18TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNDP 2017) | 2017年
基金
中国国家自然科学基金;
关键词
Conversation Generation; Deep-Learning; Long Short Term Memory (LSTM); Japanese tweets;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Human Machine Interface demands the communicative propriety that would be applied in various linguistic tasks. In this research, we develop an intelligent 'chat bot', which generates conversational sentences via recurrent neural network and its coupled memory unit, long short-term memory (LSTM). Word strings in conversations are considered as time series data. Using a single neural network model that performs a simple task of outputting the next word from the preceding word, a conversational sentence can be generated by connecting the words. In the experiment, we performed the linguistic 'Turning Test' to evaluate the proposed system.
引用
收藏
页码:483 / 487
页数:5
相关论文
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