 In this work, we propose a hierarchical conditional variational autoencoder(HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizingthe mutual information between generated QApairs to ensure their consistency.
<fluency>  In this work, we propose a hierarchical conditional variational autoencoder(HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizingthe mutual information between generated QApairs to ensure their consistency.
 In this work, we propose a hierarchical conditional variational autoencoder(HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizing the mutual information between generated QApairs to ensure their consistency.