selecting appropriate agent responses based on non-content features
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.
virtual agents machine learning behaviour selection