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Spoken dialog systems have been proposed as a solution to facilitate a more natural human-machine interaction. In this paper, we propose a framework to model the user's intention during the dialog and adapt the dialog model dynamically to the user needs and preferences, thus developing more efficient, adapted, and usable spoken dialog systems. Our framework employs statistical models based on neural networks that take into account the history of the dialog up to the current dialog state in order to predict the user's intention and the next system response. We describe our proposal and detail its application in the Let's Go spoken dialog system. (C) 2015 Elsevier B.V. All rights reserved.