This is a paraphrasing dataset created using the adversarial paradigm. A task was designed called the Adversarial Paraphrasing Task (APT) whose objective was to write sentences that mean the same as a given sentence but have as different syntactical and lexical properties as possible.

As shown in the paper, this dataset can be used to measure the performance of paraphrase identifier models and train them. This dataset and the task associated with it (APT) can also be used to challenge neural networks to generate better adversarial paraphrases (the work has done this for T5-base), which will in turn help create better paraphrase identifiers.