  We introduce CGA, a conditional VAE architecture, to control, generate, and
augment text. CGA is able to generate natural English sentences controlling
multiple semantic and syntactic attributes by combining adversarial learning
with a context-aware loss and a cyclical word dropout routine. We demonstrate
the value of the individual model components in an ablation study. The
scalability of our approach is ensured through a single discriminator,
independently of the number of attributes. We show high quality, diversity and
attribute control in the generated sentences through a series of automatic and
human assessments. As the main application of our work, we test the potential
of this new NLG model in a data augmentation scenario. In a downstream NLP
task, the sentences generated by our CGA model show significant improvements
over a strong baseline, and a classification performance often comparable to
adding same amount of additional real data.
