A Transformer-Based Variational Autoencoder for Sentence Generation

The variational autoencoder(VAE) has been proved to be a most efficient generative model, but its applications in natural language tasks have not been fully developed. A novel variational autoencoder for natural texts generation is presented in this paper. Compared to the previously introduced variational autoencoder for natural text where both the encoder and decoder are RNN-based, we propose a new transformer-based architecture and augment the decoder with an LSTM language model layer to fully exploit information of latent variables. We also propose some methods to deal with problems during training time, such as KL divergency collapsing and model degradation. In the experiment, we use random sampling and linear interpolation to test our model. Results show that the generated sentences by our approach are more meaningful and the semantics are more coherent in the latent space.