Building Dialogue POMDPs from Expert Dialogues An end-to-end approac
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
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Hamidreza Chinaei Brahim Chaib-draa
Building Dialogue POMDPs from Expert Dialogues An end-to-end approach 123
SpringerBriefs in Speech technology
More information about this series at http://www.springer.com/series/10059
Hamidreza Chinaei • Brahim Chaib-draa
Building Dialogue POMDPs from Expert Dialogues An end-to-end approach
123
Hamidreza Chinaei University of Toronto Toronto, ON, Canada
Brahim Chaib-draa Université Laval Quebec, QC, Canada
ISSN 2191-8112 ISSN 2191-8120 (electronic) SpringerBriefs in Speech technology ISBN 978-3-319-26198-0 ISBN 978-3-319-26200-0 (eBook) DOI 10.1007/978-3-319-26200-0 Library of Congress Control Number: 2015954569 Springer Cham Heidelberg New York Dordrecht London © The Authors 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www. springer.com)
Contents
1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 An End-to-End Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1 5
2
A Few Words on Topic Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Dirichlet Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Exponential Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Multinomial Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Dirichlet Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.4 Example on the Dirichlet Distribution . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Latent Dirichlet Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3
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