Dialog managers (DM) in spoken dialogue systems make decisions in highly uncertain conditions, due to errors from the speech recognition and spoken language understanding (SLU) modules. In this work a framework to interface efficient probabilistic modeling for both the SLU and the DM modules is described and investigated. Thorough representation of the user semantics is inferred by the SLU in the form of a graph of frames and, complemented with some contextual information, is mapped to a summary space in which a stochastic POMDP dialogue manager can perform planning of actions taking into account the uncertainty on the current dialogue state. Tractabil-ity is ensured by the use of an intermediate summary space. Also to reduce the development cost of SDS an approach based on clustering is proposed to automatically derive the master-summary mapping function. A preliminary implementation is presented in the Media domain (touristic information and hotel booking) and tested with a simulated user.
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