Paper: Inferring Tutorial Dialogue Structure with Hidden Markov Modeling

ACL ID W09-2103
Title Inferring Tutorial Dialogue Structure with Hidden Markov Modeling
Venue Innovative Use of NLP for Building Educational Applications
Session
Year 2009
Authors

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.