Identifying Machine-Paraphrased Plagiarism
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F22%3A43921286" target="_blank" >RIV/62156489:43110/22:43921286 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-96957-8_34" target="_blank" >https://doi.org/10.1007/978-3-030-96957-8_34</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-96957-8_34" target="_blank" >10.1007/978-3-030-96957-8_34</a>
Alternative languages
Result language
angličtina
Original language name
Identifying Machine-Paraphrased Plagiarism
Original language description
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 F1 = 71.64% for SpinnerChief cases), while human evaluators achieved F1 = 78.4% for SpinBot and F1 = 65.6% for SpinnerChief cases. We show that the automated classification alleviates shortcomings of widely-used text-matching systems, such as Turnitin and PlagScan.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Information for a Better World: Shaping the Global Future
ISBN
978-3-030-96956-1
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
21
Pages from-to
393-413
Publisher name
Springer International Publishing AG
Place of publication
Cham
Event location
Cham
Event date
Feb 28, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000772157700034