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Learning effective and engaging strategies for advice-giving human-machine dialogue

Published online by Cambridge University Press:  01 July 2009

MARTIJN SPITTERS ,
MARCO DE BONI ,
JAKUB ZAVREL  and
REMKO BONNEMA
MARTIJN SPITTERS
Affiliation:
Textkernel BV, Nieuwendammerkade 28/a17, 1022 AB Amsterdam, NL e-mail: spitters@textkernel.nl
MARCO DE BONI
Affiliation:
Unilever Corporate Research, Colworth House, Sharnbrook, Bedford, UKMK44 1LQ e-mail: marco.de-boni@unilever.com
JAKUB ZAVREL
Affiliation:
Textkernel BV, Nieuwendammerkade 28/a17, 1022 AB Amsterdam, NL
REMKO BONNEMA
Affiliation:
Textkernel BV, Nieuwendammerkade 28/a17, 1022 AB Amsterdam, NL
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Abstract

We describe a system that automatically learns effective and engaging dialogue strategies, generated from a library of dialogue content, using reinforcement learning from user feedback. Besides the more usual clarification and verification components of dialogue, this library contains various social elements like greetings, apologies, small talk, relational questions and jokes. We tested the method through an experimental dialogue system that encourages take-up of exercise and shows that the learned dialogue policy performs as well as one built by human experts for this system.

Type
Papers
Information
Natural Language Engineering , Volume 15 , Issue 3 , July 2009 , pp. 355 - 378
Copyright
Copyright © Cambridge University Press 2008

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