Minimal Text Structuring to Improve the Generation of Feedback in Intelligent Tutoring Systems

The goal of our work is to improve the Natural Language feedback provided by Intelligent Tutoring Systems. In this paper, we discuss how to make the content presented by one such system more ¤uent and comprehensible, and we show how we accomplish this by using relatively inexpensive domain-independent text structuring techniques. We show how speci£c rhetorical relations can be introduced based on the data itself in a bottom-up fashion rather than being planned top-down by the discourse planner.