  Scarcity of training data for task-oriented dialogue systems is a well known
problem that is usually tackled with costly and time-consuming manual data
annotation. An alternative solution is to rely on automatic text generation
which, although less accurate than human supervision, has the advantage of
being cheap and fast. Our contribution is twofold. First we show how to
optimally train and control the generation of intent-specific sentences using a
conditional variational autoencoder. Then we introduce a new protocol called
query transfer that allows to leverage a large unlabelled dataset, possibly
containing irrelevant queries, to extract relevant information. Comparison with
two different baselines shows that this method, in the appropriate regime,
consistently improves the diversity of the generated queries without
compromising their quality. We also demonstrate the effectiveness of our
generation method as a data augmentation technique for language modelling
tasks.
