rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Reinforcement Learning for Adaptive Dialogue Systems
    sub title: A Data-driven Methodology for Dialogue Management and Natural Language Generation
  name:
    name part: Rieser, Verena.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Lemon, Oliver.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2011
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 256 p. online resource.
  abstract: 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.
  table of contents: 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.
  note: by Verena Rieser, Oliver Lemon.
  subject:
    topic: Computer science
  subject:
    topic: User interfaces (Computer systems)
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computational linguistics
  subject:
    topic: Computer Science
  subject:
    topic: Computer Science, general
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Language Translation and Linguistics
  subject:
    topic: User Interfaces and Human Computer Interaction
  classification: QA75.5-76.95
  classification: 004
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Theory and Applications of Natural Language Processing
  identifier: 9783642249426
  identifier: http://dx.doi.org/10.1007/978-3-642-24942-6
  record info:
    record creation date: 111122
    record change date: 20151029231112.0
    record identifier: 978-3-642-24942-6