  Design of dialogue systems has witnessed many advances lately, yet acquiring
huge set of data remains an hindrance to their fast development for a new task
or language. Besides, training interactive systems with batch data is not
satisfactory. On-line learning is pursued in this paper as a convenient way to
alleviate these difficulties. After the system modules are initiated, a single
process handles data collection, annotation and use in training algorithms. A
new 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 management
modules (speech recognition and synthesis offer ready-for-use solutions). In
this context we investigate several variants of simultaneous learning which are
tested in user trials. In our experiments, with varying merits, they can all
achieve good performance with only a few hundreds of training dialogues and
overstep a handcrafted system. The analysis of these experiments gives us some
insights, discussed in the paper, into the difficulty for the system's trainers
to establish a coherent and constant behavioural strategy to enable a fast and
good-quality training phase.
