We build our method upon variational autoencoders (VAE), a deep probabilistic neural model which learns latent text features and their distributions effectively.
to other approaches using variational autoencoders for text generation, we modify the mechanism for generating new artificial samples such that we obtain samples structurally and semantically similar to a specific subset of the original data.