Reinforcement Learning for Adaptive Dialogue Systems [recurso electrónico] : A Data-driven Methodology for Dialogue Management and Natural Language Generation / by Verena Rieser, Oliver Lemon.

Por: Rieser, Verena [author.]Colaborador(es): Lemon, Oliver [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Theory and Applications of Natural Language ProcessingEditor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Descripción: XVI, 256 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783642249426Tema(s): Computer science | Artificial intelligence | Translators (Computer programs) | Computer Science | Computer Science, general | Artificial Intelligence (incl. Robotics) | Language Translation and Linguistics | User Interfaces and Human Computer InteractionFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 004 Clasificación LoC:QA75.5-76.95Recursos en línea: Libro electrónicoTexto
Contenidos:
1.Introduction -- 2.Background -- 3.Reinforcement Learning for Information Seeking dialogue strategies -- 4.The bootstrapping approach to developing Reinforcement Learning-based  strategies -- 5.Data Collection in aWizard-of-Oz experiment -- 6.Building a simulated learning environment from Wizard-of-Oz data -- 7.Comparing Reinforcement and Supervised Learning of dialogue policies with real users -- 8.Meta-evaluation -- 9.Adaptive Natural Language Generation -- 10.Conclusion -- References -- Example Dialogues -- A.1.Wizard-of-Oz Example Dialogues -- A.2.Example Dialogues from Simulated Interaction -- A.3.Example Dialogues from User Testing -- Learned State-Action Mappings -- Index.
En: Springer eBooksResumen: 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 book is a unique contribution to that ongoing change. A new  methodology for developing spoken dialogue systems is described in detail. The journey starts and ends with human behaviour in interaction, and explores methods for learning from the data, for building simulation environments for training and testing systems, and for evaluating the results. The detailed material covers: Spoken and Multimodal dialogue systems, Wizard-of-Oz data collection, User Simulation methods, Reinforcement Learning, and Evaluation methodologies. The book is a research guide for students and researchers with a background in Computer Science, AI, or Machine Learning. It navigates through a detailed case study in data-driven methods for development and evaluation of spoken dialogue systems. Common challenges associated with this approach are discussed and example solutions are provided. This work provides insights, lessons, and inspiration for future research and development – not only for spoken dialogue systems in particular, but for data-driven approaches to human-machine interaction in general.
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Tipo de ítem Biblioteca actual Colección Signatura Copia número Estado Fecha de vencimiento Código de barras
Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos QA75.5 -76.95 (Browse shelf(Abre debajo)) 1 No para préstamo 376847-2001

1.Introduction -- 2.Background -- 3.Reinforcement Learning for Information Seeking dialogue strategies -- 4.The bootstrapping approach to developing Reinforcement Learning-based  strategies -- 5.Data Collection in aWizard-of-Oz experiment -- 6.Building a simulated learning environment from Wizard-of-Oz data -- 7.Comparing Reinforcement and Supervised Learning of dialogue policies with real users -- 8.Meta-evaluation -- 9.Adaptive Natural Language Generation -- 10.Conclusion -- References -- Example Dialogues -- A.1.Wizard-of-Oz Example Dialogues -- A.2.Example Dialogues from Simulated Interaction -- A.3.Example Dialogues from User Testing -- Learned State-Action Mappings -- Index.

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 book is a unique contribution to that ongoing change. A new  methodology for developing spoken dialogue systems is described in detail. The journey starts and ends with human behaviour in interaction, and explores methods for learning from the data, for building simulation environments for training and testing systems, and for evaluating the results. The detailed material covers: Spoken and Multimodal dialogue systems, Wizard-of-Oz data collection, User Simulation methods, Reinforcement Learning, and Evaluation methodologies. The book is a research guide for students and researchers with a background in Computer Science, AI, or Machine Learning. It navigates through a detailed case study in data-driven methods for development and evaluation of spoken dialogue systems. Common challenges associated with this approach are discussed and example solutions are provided. This work provides insights, lessons, and inspiration for future research and development – not only for spoken dialogue systems in particular, but for data-driven approaches to human-machine interaction in general.

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