Modeling dialogue structure with adjacency pair analysis and hidden Markov models

Authors:
Kristy Elizabeth Boyer;Robert Phillips;Eun Young Ha;Michael D. Wallis;Mladen A. Vouk;James C. Lester
Affiliations:
North Carolina State University, Raleigh, NC;North Carolina State University, Raleigh, NC and Applied Research Associates, Raleigh, NC;North Carolina State University, Raleigh, NC;North Carolina State University, Raleigh, NC and Applied Research Associates, Raleigh, NC;North Carolina State University, Raleigh, NC;North Carolina State University, Raleigh, NC
Venue:
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Year:
2009

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Abstract

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 human-human tutorial dialogue is first manually annotated with dialogue acts. Dependent adjacency pairs of these acts are then identified through X2 analysis, and hidden Markov modeling is applied to the observed sequences to induce a descriptive model of the dialogue structure.