  Large scale Pre-trained Language Models have proven to be very powerful
approach in various Natural language tasks. OpenAI's GPT-2
\cite{radford2019language} is notable for its capability to generate fluent,
well formulated, grammatically consistent text and for phrase completions. In
this paper we leverage this generation capability of GPT-2 to generate
paraphrases without any supervision from labelled data. We examine how the
results compare with other supervised and unsupervised approaches and the
effect of using paraphrases for data augmentation on downstream tasks such as
classification. Our experiments show that paraphrases generated with our model
are of good quality, are diverse and improves the downstream task performance
when used for data augmentation.
