  The rise of language models such as BERT allows for high-quality text
paraphrasing. This is a problem to academic integrity, as it is difficult to
differentiate between original and machine-generated content. We propose a
benchmark consisting of paraphrased articles using recent language models
relying on the Transformer architecture. Our contribution fosters future
research of paraphrase detection systems as it offers a large collection of
aligned original and paraphrased documents, a study regarding its structure,
classification experiments with state-of-the-art systems, and we make our
findings publicly available.
