Modeling Dialogue Structure with Adjacency Pair Analysis and Hidden Markov Models

Automatically detecting dialogue structure within corpora of human-human dialogue is the subject of increasing attention. In the domain of tutorial dialogue, automatic discovery of dialogue structure is of particular interest because these structures inherently represent tutorial strategies or modes, the study of which is key to the design of intelligent tutoring systems that communicate with learners through natural language. We propose a methodology in which a corpus of humanhuman tutorial dialogue is first manually annotated with dialogue acts. Dependent adjacency pairs of these acts are then identified through χ 2 analysis, and hidden Markov modeling is applied to the observed sequences to induce a descriptive model of the dialogue