Label Dependent Deep Variational Paraphrase Generation

Generating paraphrases that are lexically similar but sema nti-cally different is a challenging task. Paraphrases of this f orm can be used to augment data sets for various NLP tasks such as machine reading comprehension and question answering with nontrivial negative examples. In this article, we pro - pose a deep variational model to generate paraphrases conditioned on a label that specifies whether the paraphrases are semantically related or not. We also present new training recipes and KL regularization techniques that improve the performance of variational paraphrasing models. Our pr o-posed model demonstrates promising results in enhancing th e generative power of the model by employing label-dependent generation on paraphrasing datasets.