C4.5: programs for machine learning
C4.5: programs for machine learning
Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The reliability of a dialogue structure coding scheme
Computational Linguistics
When do we interact multimodally?: cognitive load and multimodal communication patterns
Proceedings of the 6th international conference on Multimodal interfaces
Evaluating Discourse and Dialogue Coding Schemes
Computational Linguistics
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
Using machine learning to explore human multimodal clarification strategies
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Hybrid reinforcement/supervised learning of dialogue policies from fixed data sets
Computational Linguistics
Natural language generation as planning under uncertainty for spoken dialogue systems
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
The SAMMIE multimodal dialogue corpus meets the nite XML toolkit
NLPXML '06 Proceedings of the 5th Workshop on NLP and XML: Multi-Dimensional Markup in Natural Language Processing
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Classifying dialogue in high-dimensional space
ACM Transactions on Speech and Language Processing (TSLP)
Prototyping virtual instructors from human-human corpora
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Systems Demonstrations
Giving instructions in virtual environments by corpus based selection
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
CL system: giving instructions by corpus based selection
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
Corpus-based interpretation of instructions in virtual environments
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Building virtual guides for virtual worlds
HCITOCH'11 Proceedings of the Second international conference on Human-Computer Interaction, Tourism and Cultural Heritage
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We investigate the use of different machine learning methods in combination with feature selection techniques to explore human multimodal dialogue strategies and the use of those strategies for automated dialogue systems. We learn policies from data collected in a Wizard-of-Oz study where different human ‘wizards’ decide whether to ask a clarification request in a multimodal manner or else to use speech alone. We first describe the data collection, the coding scheme and annotated corpus, and the validation of the multimodal annotations. We then show that there is a uniform multimodal dialogue strategy across wizards, which is based on multiple features in the dialogue context. These are generic features, available at runtime, which can be implemented in dialogue systems. Our prediction models (for human wizard behaviour) achieve a weighted f-score of 88.6 per cent (which is a 25.6 per cent improvement over the majority baseline). We interpret and discuss the learned strategy. We conclude that human wizard behaviour is not optimal for automatic dialogue systems, and argue for the use of automatic optimization methods, such as Reinforcement Learning. Throughout the investigation we also 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 dialogue strategies from such limited data.