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

The goal of our work is to improv ethe Natural Language feedback provided by Intelligent Tutoring Systems. In this paper, we discuss how to make the content presented by one such system more fluentandcom prehensible, and we show how we accomplish this by using relatively inexpensive domain-independent text structuring techniques. W esho who wspecifi crhetoricalrelations can be introduced based on the data itself in a bottom-up fashion rather than being planned top-down by the discourse planner.

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