  Paraphrase generation is an important and challenging natural language
processing (NLP) task. In this work, we propose a deep generative model to
generate paraphrase with diversity. Our model is based on an encoder-decoder
architecture. An additional transcoder is used to convert a sentence into its
paraphrasing latent code. The transcoder takes an explicit pattern embedding
variable as condition, so diverse paraphrase can be generated by sampling on
the pattern embedding variable. We use a Wasserstein GAN to align the
distributions of the real and generated paraphrase samples. We propose a
multi-class extension to the Wasserstein GAN, which allows our generative model
to learn from both positive and negative samples. The generated paraphrase
distribution is forced to get closer to the positive real distribution, and be
pushed away from the negative distribution in Wasserstein distance. We test our
model in two datasets with both automatic metrics and human evaluation. Results
show that our model can generate fluent and reliable paraphrase samples that
outperform the state-of-art results, while also provides reasonable variability
and diversity.
