Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection
arXiv:2103.12450 · doi:10.1109/JCDL52503.2021.00065
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.
References in corpus (1)
Cited by in corpus (5)
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