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Previous question-answer pair generation methods aimed to produce fluent andmeaningful question-answer pairs but tend to have poor diversity. Recentattempts addressing this issue suffer from either low model capacity orovercomplicated architecture. Furthermore, they overlooked the problem wherethe controllability of their models is highly dependent on the input. In thispaper, we propose a model named VOLTA that enhances generative diversity byleveraging the Variational Autoencoder framework with a shared backbone networkas its encoder and decoder. In addition, we propose adding InfoGAN-style latentcodes to enable input-independent controllability over the generation process.We perform comprehensive experiments and the results show that our approach cansignificantly improve diversity and controllability over state-of-the-artmodels.

VOLTA: Diverse and Controllable Question-Answer Pair Generation with Variational Mutual Information Maximizing Autoencoder