Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Learning the structure of task-driven human-human dialogs
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Automatic annotation of context and speech acts for dialogue corpora
Natural Language Engineering
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
Empirical verification of adjacency pairs using dialogue segmentation
SigDIAL '06 Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue
Discourse structure and performance analysis: beyond the correlation
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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
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
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
| Hi-index | 0.00 |
Human dialogue serves as a valuable model for learning the behavior of dialogue systems. Hidden Markov models' sequential structure is well suited to modeling human dialogue, and their theoretical underpinnings are consistent with the conception of dialogue as a stochastic process with a layer of implicit, highly influential structure. HMMs have been shown to be effective for a variety of descriptive and predictive dialogue tasks. For task-oriented dialogue, understanding the learning behavior of HMMs is an important step toward building unsupervised models of human dialogue. This paper examines the behavior of HMMs under six experimental conditions including different task-oriented feature sets and preprocessing approaches. The findings highlight the importance of providing HMM learning algorithms with rich task-based information. Additionally, the results suggest how specific metrics should be used depending on whether the models will be employed primarily in a descriptive or predictive manner.