Building Dialogue POMDPs from Expert Dialogues An end-to-end approach /

This book discusses the Partially Observable Markov Decision Process (POMDP) framework applied in dialogue systems. It presents POMDP as a formal framework to represent uncertainty explicitly while supporting automated policy solving. The authors propose and implement an end-to-end learning approach...

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Bibliographic Details
Main Authors: Chinaei, Hamidreza (Author), Chaib-draa, Brahim (Author)
Corporate Author: SpringerLink (Online service)
Format: e-Book
Language:English
Published: Cham : Springer International Publishing : 2016.
Imprint: Springer,
Edition:1st ed. 2016.
Series:SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,
Subjects:
Signal processing.
User interfaces (Computer systems).
Human-computer interaction.
Telecommunication.
Artificial intelligence.
Computational linguistics.
Signal, Speech and Image Processing.
User Interfaces and Human Computer Interaction.
Communications Engineering, Networks.
Artificial Intelligence.
Computational Linguistics.
Online Access:Full-text access
Table of Contents:
  • 1 Introduction
  • 2 A few words on topic modeling
  • 3 Sequential decision making in spoken dialog management
  • 4 Learning the dialog POMDP model components
  • 5 Learning the reward function
  • 6 Application on healthcare dialog management
  • 7 Conclusions and future work.

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