Selecting appropriate agent responses based on non-content features

Authors:
Mark ter Maat;Dirk Heylen
Affiliations:
University of Twente, Enschede, Netherlands;University of Twente, Enschede, Netherlands
Venue:
Proceedings of the 3rd international workshop on Affective interaction in natural environments
Year:
2010

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Abstract

This paper describes work-in-progress on a study to create models of responses of virtual agents that are selected only based on non-content features, such as prosody and facial expressions. From a corpus of human-human interactions, in which one person was playing the part of an agent and the second person a user, we extracted the turns of the user and gave these to annotators. The annotators had to select utterances from a list of phrases in the repertoire of our agent that would be a good response to the user utterance. The corpus is used to train response selection models based on automatically extracted features and on human annotations of the user-turns.