Complex Probabilistic Modeling with Recursive Relational Bayesian Networks
Annals of Mathematics and Artificial Intelligence
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
An architecture for a generic dialogue shell
Natural Language Engineering
Information state and dialogue management in the TRINDI dialogue move engine toolkit
Natural Language Engineering
An agent-based approach to dialogue management in personal assistants
Proceedings of the 10th international conference on Intelligent user interfaces
Tractable planning under uncertainty: exploiting structure
Tractable planning under uncertainty: exploiting structure
Machine Learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Dialogue act recognition using maximum entropy
Journal of the American Society for Information Science and Technology
Hybrid reinforcement/supervised learning of dialogue policies from fixed data sets
Computational Linguistics
The RavenClaw dialog management framework: Architecture and systems
Computer Speech and Language
An overview of spoken language technology for education
Speech Communication
Review: Statistical parametric speech synthesis
Speech Communication
ITSPOKE: an intelligent tutoring spoken dialogue system
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Agenda-based user simulation for bootstrapping a POMDP dialogue system
NAACL-Short '07 Human Language Technologies 2007: The 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
Evaluation of a hierarchical reinforcement learning spoken dialogue system
Computer Speech and Language
The Knowledge Engineering Review
Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems
Computer Speech and Language
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Representing uncertainty about complex user goals in statistical dialogue systems
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
'How was your day?': an affective companion ECA prototype
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Planning with noisy probabilistic relational rules
Journal of Artificial Intelligence Research
A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes
The Journal of Machine Learning Research
Hierarchical solution of Markov decision processes using macro-actions
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Recognizing disfluencies in conversational speech
IEEE Transactions on Audio, Speech, and Language Processing
Probabilistic multiparty dialogue management for a game master robot
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
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Probabilistic models such as Bayesian Networks are now in widespread use in spoken dialogue systems, but their scalability to complex interaction domains remains a challenge. One central limitation is that the state space of such models grows exponentially with the problem size, which makes parameter estimation increasingly difficult, especially for domains where only limited training data is available. In this paper, we show how to capture the underlying structure of a dialogue domain in terms of probabilistic rules operating on the dialogue state. The probabilistic rules are associated with a small, compact set of parameters that can be directly estimated from data. We argue that the introduction of this abstraction mechanism yields probabilistic models that are easier to learn and generalise better than their unstructured counterparts. We empirically demonstrate the benefits of such an approach learning a dialogue policy for a human-robot interaction domain based on a Wizard-of-Oz data set.