A maximum entropy approach to natural language processing
Computational Linguistics
An architecture for more realistic conversational systems
Proceedings of the 6th international conference on Intelligent user interfaces
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
NLTK: the natural language toolkit
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Unsupervised topic modelling for multi-party spoken discourse
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Inter-coder agreement for computational linguistics
Computational Linguistics
Combining lexical, syntactic and prosodic cues for improved online dialog act tagging
Computer Speech and Language
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
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
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
The Knowledge Engineering Review
Leveraging hidden dialogue state to select tutorial moves
IUNLPBEA '10 Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications
Learning the Structure of Task-Driven Human–Human Dialogs
IEEE Transactions on Audio, Speech, and Language Processing
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
Combining verbal and nonverbal features to overcome the 'information gap' in task-oriented dialogue
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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Classifying the dialogue act of a user utterance is a key functionality of a dialogue management system. This paper presents a data-driven dialogue act classifier that is learned from a corpus of human textual dialogue. The task-oriented domain involves tutoring in computer programming exercises. While engaging in the task, students generate a task event stream that is separate from and in parallel with the dialogue. To deal with this complex task-oriented dialogue, we propose a vector-based representation that encodes features from both the dialogue and the hierarchically structured task for training a maximum likelihood classifier. This classifier also leverages knowledge of the hidden dialogue state as learned separately by an HMM, which in previous work has increased the accuracy of models for predicting tutorial moves and is hypothesized to improve the accuracy for classifying student utterances. This work constitutes a step toward learning a fully data-driven dialogue management model that leverages knowledge of the user-generated task event stream.