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Design of dialogue systems has witnessed many advances lately, yet acquiringhuge set of data remains an hindrance to their fast development for a new taskor language. Besides, training interactive systems with batch data is notsatisfactory. On-line learning is pursued in this paper as a convenient way toalleviate these difficulties. After the system modules are initiated, a singleprocess handles data collection, annotation and use in training algorithms. Anew challenge is to control the cost of the on-line learning borne by the user.Our work focuses on learning the semantic parsing and dialogue managementmodules (speech recognition and synthesis offer ready-for-use solutions). Inthis context we investigate several variants of simultaneous learning which aretested in user trials. In our experiments, with varying merits, they can allachieve good performance with only a few hundreds of training dialogues andoverstep a handcrafted system. The analysis of these experiments gives us someinsights, discussed in the paper, into the difficulty for the system's trainersto establish a coherent and constant behavioural strategy to enable a fast andgood-quality training phase.

Findings from Experiments of On-line Joint Reinforcement Learning of Semantic Parser and Dialogue Manager with real Users