Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection

Jan Philip Wahle; Terry Ruas; Norman Meuschke; Bela Gipp

doi:10.1109/jcdl52503.2021.00065

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arXiv · 2103.12450

Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection

Jan Philip Wahle·Terry Ruas·Norman Meuschke·Bela Gipp

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Abstract

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.

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Jan Philip Wahle, Terry Ruas, Norman Meuschke, Bela Gipp. 2021-03-23. Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection. https://doi.org/10.1109/jcdl52503.2021.00065

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