  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. In this paper we propose a novel controlled data
generation method that could be used as a training augmentation framework for
closed-domain dialogue. 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 novel protocol called
query transfer that allows to leverage a broad, unlabelled dataset to extract
relevant information. Comparison with two different baselines shows that our
method, in the appropriate regime, consistently improves the diversity of the
generated queries without compromising their quality.
