  Generating paraphrases that are lexically similar but semantically different
is a challenging task. Paraphrases of this form can be used to augment data
sets for various NLP tasks such as machine reading comprehension and question
answering with non-trivial negative examples. In this article, we propose 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 proposed model demonstrates
promising results in enhancing the generative power of the model by employing
label-dependent generation on paraphrasing datasets.
