Many state-of-the-art solutions for the understanding of speech data
have in common to be probabilistic and to rely on machine learning
algorithms to train their models from large amount of data. The
difficulty remains in the cost of collecting and annotating such data.
Another point is the time for updating an existing model to a new domain.
Recent works showed that a zero-shot learning method allows
to bootstrap a model with good initial performance. To do so, this
method relies on exploiting both a small-sized ontological description
of the target domain and a generic word-embedding semantic
space for generalization. Then, this framework has been extended to
exploit user feedbacks to refine the zero-shot semantic parser parameters
and increase its performance online. In this paper, we propose
to drive this online adaptive process with a policy learnt using the
Adversarial Bandit algorithm Exp3. We show, on the second Dialog
State Tracking Challenge (DSTC2) datasets, that this proposition
can optimally balance the cost of gathering valuable user feedbacks
and the overall performance of the spoken language understanding
module.