  Paraphrase generation is an interesting and challenging NLP task which has
numerous practical applications. In this paper, we analyze datasets commonly
used for paraphrase generation research, and show that simply parroting input
sentences surpasses state-of-the-art models in the literature when evaluated on
standard metrics. Our findings illustrate that a model could be seemingly adept
at generating paraphrases, despite only making trivial changes to the input
sentence or even none at all.
