  Current approaches in paraphrase generation and detection heavily rely on a
single general similarity score, ignoring the intricate linguistic properties
of language. This paper introduces two new tasks to address this shortcoming by
considering paraphrase types - specific linguistic perturbations at particular
text positions. We name these tasks Paraphrase Type Generation and Paraphrase
Type Detection. Our results suggest that while current techniques perform well
in a binary classification scenario, i.e., paraphrased or not, the inclusion of
fine-grained paraphrase types poses a significant challenge. While most
approaches are good at generating and detecting general semantic similar
content, they fail to understand the intrinsic linguistic variables they
manipulate. Models trained in generating and identifying paraphrase types also
show improvements in tasks without them. In addition, scaling these models
further improves their ability to understand paraphrase types. We believe
paraphrase types can unlock a new paradigm for developing paraphrase models and
solving tasks in the future.
