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003 DE-He213
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007 cr nn 008mamaa
008 121026s2012 xxu| s |||| 0|eng d
020 _a9781461448037
_9978-1-4614-4803-7
024 7 _a10.1007/978-1-4614-4803-7
_2doi
050 4 _aQA76.9.U83
050 4 _aQA76.9.H85
072 7 _aUYZG
_2bicssc
072 7 _aCOM070000
_2bisacsh
082 0 4 _a005.437
_223
082 0 4 _a4.019
_223
100 1 _aLemon, Oliver.
_eeditor.
245 1 0 _aData-Driven Methods for Adaptive Spoken Dialogue Systems
_h[electronic resource] :
_bComputational Learning for Conversational Interfaces /
_cedited by Oliver Lemon, Olivier Pietquin.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2012.
300 _aIX, 177 p. 35 illus., 8 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 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.
520 _aThe 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.
650 0 _aComputer science.
650 0 _aComputer simulation.
650 0 _aComputational linguistics.
650 1 4 _aComputer Science.
650 2 4 _aUser Interfaces and Human Computer Interaction.
650 2 4 _aComputational Linguistics.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aSimulation and Modeling.
650 2 4 _aArithmetic and Logic Structures.
700 1 _aPietquin, Olivier.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461448020
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-4803-7
912 _aZDB-2-SCS
950 _aComputer Science (Springer-11645)
999 _c12946
_d12946