Word Embedding for High Performance Cross-Language Plagiarism Detection Techniques

Abstract

Academic plagiarism has become a serious concern as it leads to the retardation of scientific progress and violation of intellectual property. In this context, we make a study aiming at the detection of cross-linguistic plagiarism based on Natural language Preprocessing (NLP), Embedding Techniques, and Deep Learning. Many systems have been developed to tackle this problem, and many rely on machine learning and deep learning methods. In this paper, we propose Cross-language Plagiarism Detection (CL-PD) method based on Doc2Vec embedding techniques and a Siamese Long Short-Term Memory (SLSTM) model. Embedding techniques help capture the text's contextual meaning and improve the CL-PD system's performance. To show the effectiveness of our method, we conducted a comparative study with other techniques such as GloVe, FastText, BERT, and Sen2Vec on a dataset combining PAN11, JRC-Acquis, Europarl, and Wikipedia. The experiments for the Spanish-English language pair show that Doc2Vec+SLSTM achieve the best results compared to other relevant models, with an accuracy of 99.81%, a precision of 99.75%, a recall of 99.88%, an f-score of 99.70%, and a very small loss in the test phase.

Educational Data Mining and Learning Analytics: Text Generators Usage Effect on Students’ Grades

“…Novel solutions are needed to determine whether students wrote the texts themselves or used generators. Although there are some studies performed for plagiarism detection for the English language (Khaled & Al-Tamimi, 2021) or for cross-languages (Bouaine et al, 2023), few exist for the Lithuanian language (Stefanovič et al, 2024). Although more studies are performed for text mining in most spoken languages such as English, less popular languages are not analyzed as often.…”

“…The latest area in EDM is the analysis of students' unfair behavior, where answers are generated by chatbots rather than written by students (Bouaine et al, 2023;Stefanovič et al, 2024). In the field of LA, little attention is paid to estimating how chatbot use affects student evaluation.…”

Students’ Composition Evaluation Model Based on Natural Language Processing Algorithm

“…In recent years, the research of NLP-based students' composition evaluation models has become a hot topic. Use of NLP technology effectively improves the evaluation efficiency and reduces the burden on teachers, because it is subjective, time consuming, and labor intensive to evaluate the compositions of students [6][7][8][9][10][11].…”