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DOI: 10.14569/IJACSA.2023.0140868
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A Population-based Plagiarism Detection using DistilBERT-Generated Word Embedding

Author 1: Yuqin JING
Author 2: Ying LIU

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 8, 2023.

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Abstract: Plagiarism is the unacknowledged use of another person’s language, information, or writing without crediting the source. This manuscript presents an innovative method for detecting plagiarism utilizing attention mechanism-based LSTM and the DistilBERT model, enhanced by an enriched differential evolution (DE) algorithm for pre-training and a focal loss function for training. DistilBERT reduces BERT’s size by 40% while maintaining 97% of its language comprehension abilities and being 60% quicker. Current algorithms utilize positive-negative pairs to train a two-class classifier that detects plagiarism. A positive pair consists of a source sentence and a suspicious sentence, while a negative pair comprises two dissimilar sentences. Negative pairs typically outnumber positive pairs, leading to imbalanced classification and significantly lower system performance. To combat this, a training method based on a focal loss (FL) is suggested, which carefully learns minority class examples. Another addressed issue is the training phase, which typically uses gradient-based methods like back-propagation for the learning process. As a result, the training phase has limitations, such as initialization sensitivity. A new DE algorithm is proposed to initiate the back-propagation process by employing a mutation operator based on clustering. A successful cluster for the current DE population is found, and a fresh updating approach is used to produce potential solutions. The proposed method is assessed using three datasets: SNLI, MSRP, and SemEval2014. The model attains excellent results that outperform other deep models, conventional, and population-based models. Ablation studies excluding the proposed DE and focal loss from the model confirm the independent positive incremental impact of these components on model performance.

Keywords: Plagiarism detection; LSTM; imbalanced classification; DistilBERT; differential evolution; focal loss

Yuqin JING and Ying LIU, “A Population-based Plagiarism Detection using DistilBERT-Generated Word Embedding” International Journal of Advanced Computer Science and Applications(IJACSA), 14(8), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140868

@article{JING2023,
title = {A Population-based Plagiarism Detection using DistilBERT-Generated Word Embedding},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140868},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140868},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {8},
author = {Yuqin JING and Ying LIU}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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