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Identifying Machine-Paraphrased Plagiarism. (arXiv:2103.11909v6 [cs.CL] UPDATED)
Nov. 11, 2022, 2:15 a.m. | Jan Philip Wahle, Terry Ruas, Tomáš Foltýnek, Norman Meuschke, Bela Gipp
cs.CL updates on arXiv.org arxiv.org
Employing paraphrasing tools to conceal plagiarized text is a severe threat
to academic integrity. To enable the detection of machine-paraphrased text, we
evaluate the effectiveness of five pre-trained word embedding models combined
with machine learning classifiers and state-of-the-art neural language models.
We analyze preprints of research papers, graduation theses, and Wikipedia
articles, which we paraphrased using different configurations of the tools
SpinBot and SpinnerChief. The best performing technique, Longformer, achieved
an average F1 score of 80.99% (F1=99.68% for SpinBot and …
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