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.

本文提出了一种基于编码-解码架构的深度生成模型，采用附加的转换器将句子转换为其重述潜在代码并使用 Wassertein GAN 对真实和生成的语义组句样本的分布进行比较，结果表明模型可生成具有可靠性和流畅性的变化丰富的语义重述样本，并取得了最先进的结果。

基于多类Wasserstein GAN的多样化转述生成