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Dataset aids in plagiarism detection in Kazakh, using transformer models for improved accuracy.
To the best of our knowledge, we introduce the first dataset, which was created with the intention of extrinsic plagiarism detection in Kazakh. The PAN-KK was developed through translation of over 20,000 pairs of plagiarised text in English that were in PAN 2010 and it includes a human-verified subsample of 2,000 samples, each being scrutinised by 10 native Kazakh speakers. We prepare and evaluate a range of different transformer-based models using PAN-KK and the most successful model is XLM-RoBERTa Large. As an example of practical use, we next use XLM-RoBERTa Large in a two-stage pipeline which integrates candidate retrieval with semantic verification and span localisation. In 1000 pairs that are manually annotated, the localisation of the tokens is P=0.929, R=0.843, F1=0.868, and the localisation of the spans is P=0.718, R=0.649, F1=0.667. In general, the aid of PAN–KK and its pipeline facilitates the development of the field of plagiarism detection in low-resource languages such as Kazakh.
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Bakiyev et al. (2026) studied this question.
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