  Despite many recent advances for the design of dialogue systems, a true
bottleneck remains the acquisition of data required to train its components.
Unlike many other language processing applications, dialogue systems require
interactions with users, therefore it is complex to develop them with
pre-recorded data. Building on previous works, on-line learning is pursued here
as a most convenient way to address the issue. Data collection, annotation and
use in learning algorithms are performed in a single process. The main
difficulties are then: to bootstrap an initial basic system, and to control the
level of additional cost on the user side. Considering that well-performing
solutions can be used directly off the shelf for speech recognition and
synthesis, the study is focused on learning the spoken language understanding
and dialogue management modules only. Several variants of joint learning are
investigated and tested with user trials to confirm that the overall on-line
learning can be obtained after only a few hundred training dialogues and can
overstep an expert-based system.
