The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Automatically predicting dialogue structure using prosodic features
Speech Communication - Dialogue and prosody
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Correlations between dialogue acts and learning in spoken tutoring dialogues
Natural Language Engineering
Hybrid reinforcement/supervised learning of dialogue policies from fixed data sets
Computational Linguistics
Incremental parsing models for dialog task structure
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
KSC-PaL: a peer learning agent that encourages students to take the initiative
EdAppsNLP '09 Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications
Comparing user simulation models for dialog strategy learning
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Exploring affect-context dependencies for adaptive system development
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Modeling dialogue structure with adjacency pair analysis and hidden Markov models
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
The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management
Computer Speech and Language
Adapting to Student Uncertainty Improves Tutoring Dialogues
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
To Elicit Or To Tell: Does It Matter?
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
The Knowledge Engineering Review
Characterizing the effectiveness of tutorial dialogue with hidden markov models
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Learning the Structure of Task-Driven Human–Human Dialogs
IEEE Transactions on Audio, Speech, and Language Processing
AutoTutor: an intelligent tutoring system with mixed-initiative dialogue
IEEE Transactions on Education
Dialogue act modeling in a complex task-oriented domain
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Classifying dialogue in high-dimensional space
ACM Transactions on Speech and Language Processing (TSLP)
An affect-enriched dialogue act classification model for task-oriented dialogue
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
The impact of task-oriented feature sets on HMMs for dialogue modeling
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
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A central challenge for tutorial dialogue systems is selecting an appropriate move given the dialogue context. Corpus-based approaches to creating tutorial dialogue management models may facilitate more flexible and rapid development of tutorial dialogue systems and may increase the effectiveness of these systems by allowing data-driven adaptation to learning contexts and to individual learners. This paper presents a family of models, including first-order Markov, hidden Markov, and hierarchical hidden Markov models, for predicting tutor dialogue acts within a corpus. This work takes a step toward fully data-driven tutorial dialogue management models, and the results highlight important directions for future work in unsupervised dialogue modeling.