Presentation 2019-11-28
Emotion Learning from Emotion Tags and Comparative Experience with LSTM and GRU
Kuniaki Yuba, Eisuke Ito,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Dialogue Programs have improved their performances by using GPU and Servers.But, their characters are low diversity and there aren’t services to create their programs for users. So, we pay attention to Desktop Mascots that were created for users and high diversity and try to solve this problem by incorporating dialogue technology into these Desktop Mascot. As a starting point, we decided to conduct Emotional learnings and Comparative Experiences LSTM and GRU.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Seq2Seq / LSTM / GRU / Emortional Learning / Desktop Mascot
Paper # AI2019-35
Date of Issue 2019-11-21 (AI)

Conference Information
Committee AI
Conference Date 2019/11/28(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Naoki Fukuta(Shizuoka Univ.)
Vice Chair Yuichi Sei(Univ. of Electro-Comm.) / Yuko Sakurai(AIST)
Secretary Yuichi Sei(Osaka Univ.) / Yuko Sakurai(Tokyo Univ. of Agriculture and Technology)

Paper Information
Registration To Technical Committee on Artificial Intelligence and Knowledge-Based Processing
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Emotion Learning from Emotion Tags and Comparative Experience with LSTM and GRU
Sub Title (in English) Heading toward Sublimation from Desktop Mascot to AI Agent
Keyword(1) Seq2Seq
Keyword(2) LSTM
Keyword(3) GRU
Keyword(4) Emortional Learning
Keyword(5) Desktop Mascot
1st Author's Name Kuniaki Yuba
1st Author's Affiliation Kyushu University(Kyudai)
2nd Author's Name Eisuke Ito
2nd Author's Affiliation Kyushu University(Kyudai)
Date 2019-11-28
Paper # AI2019-35
Volume (vol) vol.119
Number (no) AI-317
Page pp.pp.31-36(AI),
#Pages 6
Date of Issue 2019-11-21 (AI)