Inferring tutorial dialogue structure with hidden Markov modeling

Authors:
Kristy Elizabeth Boyer;Eun Young Ha;Robert Phillips;Michael D. Wallis;Mladen A. Vouk;James C. Lester
Affiliations:
North Carolina State University;North Carolina State University;North Carolina State University and Applied Research Associates Raleigh, NC;North Carolina State University and Applied Research Associates Raleigh, NC;North Carolina State University;North Carolina State University
Venue:
EdAppsNLP '09 Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications
Year:
2009

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Abstract

The field of intelligent tutoring systems has seen many successes in recent years. A significant remaining challenge is the automatic creation of corpus-based tutorial dialogue management models. This paper reports on early work toward this goal. We identify tutorial dialogue modes in an unsupervised fashion using hidden Markov models (HMMs) trained on input sequences of manually-labeled dialogue acts and adjacency pairs. The two best-fit HMMs are presented and compared with respect to the dialogue structure they suggest; we also discuss potential uses of the methodology for future work.