Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection. Wahle, J. P., Ruas, T., Meuschke, N., & Gipp, B. In 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 226–229, September, 2021. arXiv:2103.12450 [cs]![Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 2 downloads 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.
@inproceedings{wahle_are_2021,
title = {Are {Neural} {Language} {Models} {Good} {Plagiarists}? {A} {Benchmark} for {Neural} {Paraphrase} {Detection}},
shorttitle = {Are {Neural} {Language} {Models} {Good} {Plagiarists}?},
url = {https://doi.org/10.1109/JCDL52503.2021.00065},
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.},
urldate = {2022-11-04},
booktitle = {2021 {ACM}/{IEEE} {Joint} {Conference} on {Digital} {Libraries} ({JCDL})},
author = {Wahle, Jan Philip and Ruas, Terry and Meuschke, Norman and Gipp, Bela},
month = sep,
year = {2021},
note = {arXiv:2103.12450 [cs]},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Digital Libraries},
pages = {226--229},
}
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