Conditioned Text Generation with Transfer for Closed-Domain Dialogue Systems

International audience; Scarcity of training data for task-oriented dialogue systemsis a well known problem that is usually tackled with costly and time-consuming manual data annotation. An alternative solution is to relyon automatic text generation which, although less accurate than humansupervision, has the advantage of being cheap and fast. Our contributionis twofold. First we show how to optimally train and control the generationof intent-specific sentences using a conditional variational autoencoder.Then we introduce a new protocol calledquery transferthat allows toleverage a large unlabelled dataset, possibly containing irrelevant queries,to extract relevant information. Comparison with two different baselinesshows that this method, in the appropriate regime, consistently improvesthe diversity of the generated queries without compromising their quality.We also demonstrate the effectiveness of our generation method as a dataaugmentation technique for language modelling tasks.