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This paper proposes a probabilistic framework for spoken dialog management using dialog examples. To overcome the complexity problems of the classic partially observable Markov decision processes (POMDPs) based dialog manager, we use a frame-based belief state representation that reduces the complexity of belief update. We also used dialog examples to maintain a reasonable number of system actions to reduce the complexity of the optimizing policy. We developed weather information and car navigation dialog system that employed a frame-based probabilistic framework. This framework enables people to develop a spoken dialog system using a probabilistic approach without complexity problem of POMDP.

A frame-based probabilistic framework for spoken dialog management using dialog examples