Paper Info
Title
Discovering Tutorial Dialogue Strategies with Hidden Markov Models
Abstract
Identifying effective tutorial strategies is a key problem for tutorial dialogue systems research. Ongoing work in human-human tutorial dialogue continues to reveal the complex phenomena that characterize these interactions, but we have not yet seen the emergence of an automated approach to discovering tutorial dialogue strategies. This paper presents a first step toward establishing a methodology for such an approach. In this methodology, a corpus is first annotated with dialogue acts that are grounded in theories of tutoring and natural language dialogue. Hidden Markov modeling is then applied to discover tutorial strategies inherent in the structure of the sequenced dialogue acts. The methodology is illustrated by demonstrating how hidden Markov models can be learned from a corpus of human-human tutoring in the domain of introductory computer science.
Year
DOI
Venue
2009
10.3233/978-1-60750-028-5-141
AIED
Keywords
Field
DocType
hidden markov models,tutorial dialogue strategy,sequenced dialogue act,dialogue act,hidden markov modeling,tutorial dialogue systems research,discovering tutorial dialogue strategies,tutorial strategy,natural language dialogue,human-human tutorial dialogue,automated approach,effective tutorial strategy,hidden markov model,machine learning
Dialogue acts,Computer science,Natural language,Natural language processing,Artificial intelligence,Hidden Markov model,Machine learning
Conference
Volume
ISSN
Citations 
200
0922-6389
16
PageRank 
References 
Authors
1.27
15
6
Name
Order
Citations
PageRank
Kristy Elizabeth Boyer154064.01
Eun Young Ha21358.37
Michael D. Wallis31299.12
Robert Phillips416813.02
Mladen A. Vouk545249.92
James C. Lester62398282.35