Leveraging Hidden Dialogue State to Select Tutorial Moves

A central challenge for tutorial dialogue systems is selecting an appropriate move given the dialogue context. Corpus-based approaches to creating tutorial dialogue management models may facilitate more flexible and rapid development of tutorial dialogue systems and may increase the effectiveness of these systems by allowing data-driven adaptation to learning contexts and to individual learners. This paper presents a family of models, including first-order Markov, hidden Markov, and hierarchical hidden Markov models, for predicting tutor dialogue acts within a corpus. This work takes a step toward fully data-driven tutorial dialogue management models, and the results highlight important directions for future work in unsupervised dialogue modeling.