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Feature-based summary space for stochastic dialogue modeling with hierarchical semantic frames

In a spoken dialogue system, the dialogue manager needs to make decisions in a highly noisy environment, mainly due to speech recognition and understanding errors. This work addresses this issue by proposing a framework to interface efficient probabilistic modeling for both the spoken language understanding module and the dialogue management module. First hierarchical semantic frames are inferred and composed so as to build a thorough representation of the user’s utterance semantics. Then this representation is mapped into a feature-based summary space in which is defined the set of dialogue states used by the stochastic dialogue manager, based on the partially observable Markov decision process (POMDP) paradigm. This allows a planning of the dialogue course taking into account the uncertainty on the current dialogue state and tractability is ensured by the use of an intermediate summary space. A preliminary implementation of such a system is presented on the MEDIA domain. The task is touristic information and hotel booking, and the availability of WoZ data allows to consider a model-based approach to the POMDP dialogue manager. .