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Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection. (arXiv:2103.12450v3 [cs.CL] UPDATED)
May 2, 2022, 1:11 a.m. | Jan Philip Wahle, Terry Ruas, Norman Meuschke, Bela Gipp
cs.CL updates on arXiv.org arxiv.org
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 …
More from arxiv.org / cs.CL updates on arXiv.org
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