Learning the Use of Discourse Markers in Tutorial Dialogue for an Intelligent Tutoring System

Usage of discourse markers in tutorial language can make the difference between stilted and natural sounding dialogue. In this paper we describe some simple rules for selection of discourse markers. These rules were derived for use in an intelligent tutoring system by applying decision-tree machine learning to human tutoring language. The fact that these selection rules operate within the environment of an intention-based planner encouraged us to derive our decision tree partly based on intention-based features. The resulting tree, when applied to the generation task, is relatively easy to understand because it can be referred to traditional intentionbased linguistic explanations of discourse marker behavior.