Interpretation and Generation in a Knowledge-Based TutorialSystem

Abstract

We discuss how deep interpretation and generation can be integrated with a knowledge representation designed for question answering to build a tutorial dialogue system. We use a knowledge representation known to perform well in answering exam-type questions and show that to support tutorial dialogue it needs additional features, in particular, compositional representations for interpretation and structured explanation representations.