Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management
Computer Speech and Language
A frame-based probabilistic framework for spoken dialog management using dialog examples
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems
Computer Speech and Language
Exploiting machine-transcribed dialog corpus to improve multiple dialog states tracking methods
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
A bottom-up exploration of the dimensions of dialog state in spoken interaction
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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Effective dialogue management is critically dependent on the information that is encoded in the dialogue state. In order to deploy reinforcement learning for policy optimization, dialogue must be modeled as a Markov Decision Process. This requires that the dialogue state must encode all relevent information obtained during the dialogue prior to that state. This can be achieved by combining the user goal, the dialogue history, and the last user action to form the dialogue state. In addition, to gain robustness to input errors, dialogue must be modeled as a Partially Observable Markov Decision Process (POMDP) and hence, a distribution over all possible states must be maintained at every dialogue turn. This poses a potential computational limitation since there can be a very large number of dialogue states. The Hidden Information State model provides a principled way of ensuring tractability in a POMDP-based dialogue model. The key feature of this model is the grouping of user goals into partitions that are dynamically built during the dialogue. In this article, we extend this model further to incorporate the notion of complements. This allows for a more complex user goal to be represented, and it enables an effective pruning technique to be implemented that preserves the overall system performance within a limited computational resource more effectively than existing approaches.