English-Persian cross-language plagiarism detection: Multilingual deep learning approach

Sara Ansar, Marzieh Zarinbal and Azadeh Mohebi

PLOS ONE, 2026, vol. 21, issue 9, 1-23

Abstract: Cross-lingual plagiarism detection (CLPD) remains a critical challenge in Natural Language Processing, particularly for low-resource language pairs where translation and paraphrasing obscure source materials. This study introduces a robust framework for Persian-English CLPD by integrating state-of-the-art sentence representations with optimized machine learning classifiers. We utilized the BAAI/bge-m3 model to generate high-dimensional embeddings, employing cosine similarity to measure contrastive linguistic distances. To evaluate classification performance, we compared various architectures, including XGBoost, LSTM, and Logistic Regression (LR), across three categories: Exact, Semi-Exact, and Different. While deep learning and ensemble methods were initially considered, experimental results on an expanded dataset demonstrated that a fine-tuned LR model provided superior stability and generalization. The optimized LR approach significantly increased classification accuracy from 89% to 96% (p

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0354459

DOI: 10.1371/journal.pone.0354459

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