Question Ranking and Selection in Tutorial Dialogues

A key challenge for dialogue-based intelligent tutoring systems lies in selecting follow-up questions that are not only context relevant but also encourage self-expression and stimulate learning. This paper presents an approach to ranking candidate questions for a given dialogue context and introduces an evaluation framework for this task. We learn to rank using judgments collected from expert human tutors, and we show that adding features derived from a rich, multi-layer dialogue act representation improves system performance over baseline lexical and syntactic features to a level in agreement with the judges. The experimental results highlight the important factors in modeling the questioning process. This work provides a framework for future work in automatic question generation and it represents a step toward the larger goal of directly learning tutorial dialogue policies directly from human examples.