Paraphrase Generation with Latent Bag of Words

Paraphrase generation is a longstanding important problem in natural language processing. Recent progress in deep generative models has shown promising results on discrete latent variables for text generation. Inspired by variational autoencoders with discrete latent structures, in this work, we propose a latent bag of words (BOW) model for paraphrase generation. We ground the semantics of a discrete latent variable by the target BOW. We use this latent variable to build a fully differentiable content planning and surface realization pipeline. Specifically, we use source words to predict their neighbors and model the target BOW with a mixture of softmax.