Presentation 2009/12/14
Dialogue Control by POMDP using Dialogue Data Statistics
Yasuhiro Minami, Akira Mori, Toyomi Meguro, Ryuichiro Higashinaka, Kohji Dohsaka, Eisaku Maeda,
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Abstract(in English) We propose a method that generates appropriate agent dialogue control for users by training a large amount of human to human dialogue data. We offer two technical points to resolve this issue. One is the automatic acquisition of POMDPs'(partially observable Markov decision process's)state transition probabilities, output probabilities and rewards through DBNs(Dynamic Bayesian Networks)with a large amount of dialogue data, and the other is applying rewards from the emission probabilities of agent actions into POMDPs' reinforcement learning. This paper proposes a method to simultaneously achieve purpose-oriented and stochastic naturalness-oriented action controls. Our experimental results demonstrate the effectiveness of our framework, which shows that the agent can generate both actions without being locked into either of them.
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Committee NLC
Conference Date 2009/12/14(1days)
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Registration To Natural Language Understanding and Models of Communication (NLC)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Dialogue Control by POMDP using Dialogue Data Statistics
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1st Author's Name Yasuhiro Minami
1st Author's Affiliation Nippon Telegraph and Telephone Corporation()
2nd Author's Name Akira Mori
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation
3rd Author's Name Toyomi Meguro
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation
4th Author's Name Ryuichiro Higashinaka
4th Author's Affiliation Nippon Telegraph and Telephone Corporation
5th Author's Name Kohji Dohsaka
5th Author's Affiliation Nippon Telegraph and Telephone Corporation
6th Author's Name Eisaku Maeda
6th Author's Affiliation Nippon Telegraph and Telephone Corporation
Date 2009/12/14
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Volume (vol) vol.109
Number (no) 355
Page pp.pp.-
#Pages 6
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