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Building dialogue POMDPs from expert...

Chinaei, Hamidreza.

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Building dialogue POMDPs from expert dialogues = an end-to-end approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Building dialogue POMDPs from expert dialogues/ by Hamidreza Chinaei, Brahim Chaib-draa.
Reminder of title:	an end-to-end approach /
Author:	Chinaei, Hamidreza.
other author:	Chaib-draa, Brahim.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	vii, 119 p. :ill., digital ;24 cm.
[NT 15003449]:	1 Introduction -- 2 A few words on topic modeling -- 3 Sequential decision making in spoken dialog management -- 4 Learning the dialog POMDP model components -- 5 Learning the reward function -- 6 Application on healthcare dialog management -- 7 Conclusions and future work.
Contained By:	Springer eBooks
Subject:	Natural language processing (Computer science) -
Online resource:	http://dx.doi.org/10.1007/978-3-319-26200-0
ISBN:	9783319262000$q(electronic bk.)

Building dialogue POMDPs from expert dialogues = an end-to-end approach /
Chinaei, Hamidreza.

Building dialogue POMDPs from expert dialoguesan end-to-end approach /[electronic resource] :by Hamidreza Chinaei, Brahim Chaib-draa. - Cham :Springer International Publishing :2016. - vii, 119 p. :ill., digital ;24 cm. - SpringerBriefs in electrical and computer engineering,2191-8112. - SpringerBriefs in electrical and computer engineering..

1 Introduction -- 2 A few words on topic modeling -- 3 Sequential decision making in spoken dialog management -- 4 Learning the dialog POMDP model components -- 5 Learning the reward function -- 6 Application on healthcare dialog management -- 7 Conclusions and future work.

This book discusses the Partially Observable Markov Decision Process (POMDP) framework applied in dialogue systems. It presents POMDP as a formal framework to represent uncertainty explicitly while supporting automated policy solving. The authors propose and implement an end-to-end learning approach for dialogue POMDP model components. Starting from scratch, they present the state, the transition model, the observation model and then finally the reward model from unannotated and noisy dialogues. These altogether form a significant set of contributions that can potentially inspire substantial further work. This concise manuscript is written in a simple language, full of illustrative examples, figures, and tables. Provides insights on building dialogue systems to be applied in real domain Illustrates learning dialogue POMDP model components from unannotated dialogues in a concise format Introduces an end-to-end approach that makes use of unannotated and noisy dialogue for learning each component of dialogue POMDPs.

ISBN: 9783319262000$q(electronic bk.)

Standard No.: 10.1007/978-3-319-26200-0doiSubjects--Topical Terms:

565309
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38

Dewey Class. No.: 006.35

Building dialogue POMDPs from expert dialogues = an end-to-end approach /
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520 $a This book discusses the Partially Observable Markov Decision Process (POMDP) framework applied in dialogue systems. It presents POMDP as a formal framework to represent uncertainty explicitly while supporting automated policy solving. The authors propose and implement an end-to-end learning approach for dialogue POMDP model components. Starting from scratch, they present the state, the transition model, the observation model and then finally the reward model from unannotated and noisy dialogues. These altogether form a significant set of contributions that can potentially inspire substantial further work. This concise manuscript is written in a simple language, full of illustrative examples, figures, and tables. Provides insights on building dialogue systems to be applied in real domain Illustrates learning dialogue POMDP model components from unannotated dialogues in a concise format Introduces an end-to-end approach that makes use of unannotated and noisy dialogue for learning each component of dialogue POMDPs.
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