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

• URL: http://arxiv.org/abs/2103.12450v1
• Date: Tue, 23 Mar 2021 11:01:35 GMT
• Title: Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection
• Authors: Jan Philip Wahle, Terry Ruas, Norman Meuschke, Bela Gipp
• Abstract summary: 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.
• Score: 5.847824494580938
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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