Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation
Giuseppe Russo, Nora Hollenstein, Claudiu Musat, Ce Zhang · 2020
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.