A Systematic Review of Multilingual Plagiarism Detection: Approaches and Research Challenges

The existence of voluminous multilingual sources on the web in different fields creates numerous issues, including violations of intellectual property rights. For that, the multilingual plagiarism or cross-language plagiarism detection (CLPD) has become a great challenge, which refers to copying content from a source text in one language into a target text in another without proper attribution. This study presents a systematic literature review (SLR) of methodologies used in CLPD covering works published between 2014 and 2025. This literature review summarizes and diagrams the different approaches used for CLPD. We propose a classification of the different representations of multilingual texts into four types: traditional approaches, multilingual semantic networks, fingerprinting methods, and deep learning models. In addition, we have carried out an in-depth analysis of ten language pairs and have focused on the approaches employed, including translation strategies, feature extraction approaches, classification techniques, similarity methods, dataset types, data granularity, and evaluation metrics. Among the fulfilled results, English appears in 98% of language pairs, and the English-Arabic pair stands out as the most studied. Over 60% of studies involve a translation phase with Google Translate as the most frequently used tool. The mBART model achieves over 95% accuracy for English-Spanish, English-French, and English-German, while BERT reached 96% for English-Russian. As for the assisted translation study based on the Expert translation tool, strong results are obtained for English-Persian, with an accuracy of 98.82%. On the whole, transformers offer better results in several language pairs without the need for translation.

@article{BOUAINE2025,
  title     = {A Systematic Review of Multilingual Plagiarism Detection: Approaches and Research Challenges},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Chaimaa BOUAINE and Faouzia BENABBOU and Zineb Ellaky and Amine BOUAINE and Chaimae ZAOUI},
  doi       = {10.14569/IJACSA.2025.0160836},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160836}
}