大葉大學圖書館 |

Building dialogue POMDPs from expert dialogues[electronic resource] :an end-to-end approach /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.35
書名/作者:	Building dialogue POMDPs from expert dialogues : an end-to-end approach // by Hamidreza Chinaei, Brahim Chaib-draa.
作者:	Chinaei, Hamidreza.
其他作者:	Chaib-draa, Brahim.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	vii, 119 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Natural language processing (Computer science)
標題:	Speech processing systems.
標題:	Markov processes.
標題:	Engineering.
標題:	Signal, Image and Speech Processing.
標題:	User Interfaces and Human Computer Interaction.
標題:	Communications Engineering, Networks.
標題:	Artificial Intelligence (incl. Robotics)
標題:	Computational Linguistics.
ISBN:	9783319262000
ISBN:	9783319261980
內容註:	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.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-26200-0

Building dialogue POMDPs from expert dialogues[electronic resource] :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

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

411876
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38

Dewey Class. No.: 006.35

Building dialogue POMDPs from expert dialogues[electronic resource] :an end-to-end approach /
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