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Comparing Reinforcement and Supervised Learning of Dialogue Policies with Real Users

  • Verena Rieser
  • Oliver Lemon
Chapter
Part of the Theory and Applications of Natural Language Processing book series (NLP)

Abstract

In Chapter 7 we showed that Reinforcement Learning (RL) based strategies can significantly outperform supervised strategies, in interaction with a simulated environment. The ultimate test for dialogue strategies, however, is how they perform with real users. For real users it is often difficult to complete even relatively simple tasks using automated dialogue systems.

Keywords

Reinforcement Learn Reward Function Real User Dialogue System Dialogue Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.School of Mathematical and Computer SciencesHeriot-Watt UniversityEdinburghUK

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