Reinforcement Learning for Adaptive Dialogue Systems A Data-driven M
The past decade has seen a revolution in the field of spoken dialogue systems. As in other areas of Computer Science and Artificial Intelligence, data-driven methods are now being used to drive new methodologies for system development and evaluation. This
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Aims and Scope The field of Natural Language Processing (NLP) has expanded explosively over the past decade: growing bodies of available data, novel fields of applications, emerging areas and new connections to neighboring fields have all led to increasing output and to diversification of research. “Theory and Applications of Natural Language Processing” is a series of volumes dedicated to selected topics in NLP and Language Technology. It focuses on the most recent advances in all areas of the computational modeling and processing of speech and text across languages and domains. Due to the rapid pace of development, the diversity of approaches and application scenarios are scattered in an ever-growing mass of conference proceedings, making entry into the field difficult for both students and potential users. Volumes in the series facilitate this first step and can be used as a teaching aid, advanced-level information resource or a point of reference. The series encourages the submission of research monographs, contributed volumes and surveys, lecture notes and textbooks covering research frontiers on all relevant topics, offering a platform for the rapid publication of cutting-edge research as well as for comprehensive monographs that cover the full range of research on specific problem areas. The topics include applications of NLP techniques to gain insights into the use and functioning of language, as well as the use of language technology in applications that enable communication, knowledge management and discovery such as natural language generation, information retrieval, question-answering, machine translation, localization and related fields. The books are available in printed and electronic (e-book) form: * Downloadable on your PC, e-reader or iPad * Enhanced by Electronic Supplementary Material, such as algorithms, demonstrations, software, images and videos * Available online within an extensive network of academic and corporate R&D libraries worldwide * Never out of print thanks to innovative print-on-demand services * Competitively priced print editions for eBook customers thanks to MyCopy service http://www.springer.com/librarians/e-content/mycopy
For further volumes: http://www.springer.com/series/8899
Verena Rieser • Oliver Lemon
Reinforcement Learning for Adaptive Dialogue Systems A Data-driven Methodology for Dialogue Management and Natural Language Generation
Verena Rieser School of Mathematical and Computer Sciences Heriot-Watt University Edinburgh EH14 4AS United Kingdom [email protected]
Oliver Lemon School of Mathematical and Computer Sciences Heriot-Watt University Edinburgh EH14 4AS United Kingdom [email protected]
ISSN 2192-032X e-ISSN 2192-0338 ISBN 978-3-642-24941-9 e-ISBN 978-3-642-24942-6 DOI 10.1007/978-3-642-24942-6 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011942326 Mathematics Subject Classification (2010): 68-XX, 68Txx, 68T05, 68T50, 68T37, 68T42 © Springer-Verlag Berlin Heidelberg 2011 This work is subject to copyright. All rights are res
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