6th SIGdial Workshop on Discourse and Dialogue
Current dialogue systems are fairly poor in generating the wide range of clarification strategies as found in human-human dialogue. The overall aim of this work is to learn when and how to best employ different types of clarification strategies in multimodal dialogue systems. This paper describes a framework for learning multimodal clarification strategies for an in-car MP3 music player dialogue system. The framework consists of three major parts. First we collect data on multimodal clarification strategies in a wizard-of-oz study. Second we extract feature in the stateaction space to learn an initial policy from this data. Third we specify a reward function to refine that policy using extensions of existing evaluation schemes.
Bibliographic reference. Rieser, Verena / Kruijff-Korbayová, Ivana / Lemon, Oliver (2005): "A corpus collection and annotation framework for learning multimodal clarification strategies", In SIGdial6-2005, 97-106.