Final Report Summary - COREDIAL (Spoken dialog management that combines corpus-based statistical learning and reinforcement learning with a constraint-based core) Spoken dialog systems (SDS) have made great progress over the last 10 years. Their use has become wide-spread in many areas, especially call centre automation, but also for interacting with car- based dialog systems or robots, for example. Despite this progress, significant challenges remain: human-machine communication is in practice quite different from human-human communication.The COREDIAL project addresses dialog management, a core task for spoken dialog systems that deals with making an action decision, and extends the approach to include language understanding and generation in a complete spoken dialog system. The objective is to provide a constructive proof that corpus-based statistical methods can be combined with reinforcement learning for dialog management.In the reporting period, the principal researcher Dr Sebastian Varges worked on crucial components of the planned work programme:1) Final evaluation of experimental data of a SDS.2) Textual natural language generation component. This part of the work program is already completed but will be revised if other system components require changes.3) Content-to-speech generation to drive speech synthesis: Text generation is being extended to generate text with prosodic mark-up to drive a text-to-speech system. This means that the voice can empharise parts of the speech more like a human being.4) Dialog system architecture: We extended a proven dialog system architecture to handle the demands of statistics-based dialog management.