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

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 < 0.05). These findings suggest that high-quality embeddings can enhance detection performance in low-resource contexts without the computational overhead of complex architectures. To ensure transparency and reproducibility, our dataset and source code are available on GitHub.

The authors' abstract, as published at the source. PLoS ONE, 2026 · DOI ↗