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: Data-Driven Methods for Adaptive Spoken Dialogue Systems
    sub title: Computational Learning for Conversational Interfaces
  name:
    name part: Lemon, Oliver.
    role:
      role term: editor.
  name:
    name part: Pietquin, Olivier.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: X, 178 p. online resource.
  abstract: The EC FP7 project “Computational Learning in Adaptive Systems for Spoken Conversation” (CLASSiC) was a European initiative working on a fully data-driven architecture for the development of conversational interfaces, as well as new machine learning approaches for their sub-components. It developed a variety of novel statistical methods for spoken dialogue processing, for extended conversational interaction, which are now collected together in this book. A major focus of the project was in tracking the accumulation of information about user goals over multiple dialogue turns (i.e.\ extended conversational interaction), and in maintaining overall system robustness even when speech recognition results contain errors, by managing uncertainty through the processing chain. Other advances were made in the areas of adaptive natural language generation (NLG), statistical methods for spoken language understanding (SLU), and machine learning methods for system optimisation, either during online operation, simulation, or from small amounts of data. This book collects together the main research results and lessons learned in the CLASSiC project. Each chapter provides a summary of the specific methods developed and results obtained in its particular research area. In addition, leading researchers in statistical methods applied to industrial-scale dialogue systems (from SpeechCycle) have contributed a chapter surveying their recent work. This volume will serve as a valuable introduction to the current state-of-the-art in statistical approaches to developing conversational interfaces, for active researchers in the field in industry and academia, as well as for students who are considering working in this exciting area.
  table of contents: Chapter 1. Conversational Interfaces -- Chapter 2. Developing Dialogue Managers from Limited Amounts of Data -- Chapter 3. Data-Driven Methods for Spoken Language Understanding -- Chapter 4. User Simulation in the Development of Statistical Spoken Dialogue Systems -- Chapter 5. Optimisation for POMDP-based Spoken Dialogue Systems -- Chapter 6. Statistical Approaches to Adaptive Natural Language Generation -- Chapter 7. Metrics and Evaluation of Spoken Dialogue Systems -- Chapter 8. Data-Driven Methods in Industrial Spoken Dialog Systems -- Chapter 9. Future Research Directions.
  note: edited by Oliver Lemon, Olivier Pietquin.
  subject:
    topic: Computer science
  subject:
    topic: Arithmetic and logic units, Computer
  subject:
    topic: User interfaces (Computer systems)
  subject:
    topic: Computer simulation
  subject:
    topic: Computational linguistics
  subject:
    topic: Computer Science
  subject:
    topic: User Interfaces and Human Computer Interaction
  subject:
    topic: Computational Linguistics
  subject:
    topic: Signal, Image and Speech Processing
  subject:
    topic: Simulation and Modeling
  subject:
    topic: Arithmetic and Logic Structures
  classification: QA76.9.U83
  classification: QA76.9.H85
  classification: 005.437
  classification: 4.019
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781461448037
  identifier: http://dx.doi.org/10.1007/978-1-4614-4803-7
  record info:
    record creation date: 121026
    record change date: 20151125191958.0
    record identifier: 978-1-4614-4803-7