C4.5: programs for machine learning
C4.5: programs for machine learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
When do we interact multimodally?: cognitive load and multimodal communication patterns
Proceedings of the 6th international conference on Multimodal interfaces
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Implications for generating clarification requests in task-oriented dialogues
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Learning more effective dialogue strategies using limited dialogue move features
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Hybrid reinforcement/supervised learning of dialogue policies from fixed data sets
Computational Linguistics
Automatic annotation of context and speech acts for dialogue corpora
Natural Language Engineering
Learning effective and engaging strategies for advice-giving human-machine dialogue
Natural Language Engineering
Using Graphical Models for an Intelligent Mixed-Initiative Dialog Management System
Proceedings of the Symposium on Human Interface 2009 on Human Interface and the Management of Information. Information and Interaction. Part II: Held as part of HCI International 2009
Learning human multimodal dialogue strategies
Natural Language Engineering
Towards a programmable instrumented generator
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
User Modeling and User-Adapted Interaction
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Data mining to support human-machine dialogue for autonomous agents
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
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We investigate the use of machine learning in combination with feature engineering techniques to explore human multimodal clarification strategies and the use of those strategies for dialogue systems. We learn from data collected in a Wizard-of-Oz study where different wizards could decide whether to ask a clarification request in a multimodal manner or else use speech alone. We show that there is a uniform strategy across wizards which is based on multiple features in the context. These are generic runtime features which can be implemented in dialogue systems. Our prediction models achieve a weighted f-score of 85.3% (which is a 25.5% improvement over a one-rule baseline). To assess the effects of models, feature discretisation, and selection, we also conduct a regression analysis. We then interpret and discuss the use of the learnt strategy for dialogue systems. Throughout the investigation we discuss the issues arising from using small initial Wizard-of-Oz data sets, and we show that feature engineering is an essential step when learning from such limited data.