  We present a simple and effective way to generate a variety of paraphrases
and find a good quality paraphrase among them. As in previous studies, it is
difficult to ensure that one generation method always generates the best
paraphrase in various domains. Therefore, we focus on finding the best
candidate from multiple candidates, rather than assuming that there is only one
combination of generative models and decoding options. Our approach shows that
it is easy to apply in various domains and has sufficiently good performance
compared to previous methods. In addition, our approach can be used for data
augmentation that extends the downstream corpus, showing that it can help
improve performance in English and Korean datasets.
