In this article we study a stochastic model for spoken dialogue systems (SDSs). We advocate 
filtering methods, and in particular smoothing to be used in SDSs and argue that these methods let one design (or learn) dialogue control strategies whereas the number of unnatural (and unnecessary) clarification requests can be reduced, resulting in more natural dialogues.