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Detecting plagiarism involves finding similar items in two different sources.In this article, we propose a novel method for detecting plagiarism that isbased on attention mechanism-based long short-term memory (LSTM) andbidirectional encoder representations from transformers (BERT) word embedding,enhanced with optimized differential evolution (DE) method for pre-training anda focal loss function for training. BERT could be included in a downstream taskand fine-tuned as a task-specific BERT can be included in a downstream task andfine-tuned as a task-specific structure, while the trained BERT model iscapable of detecting various linguistic characteristics. Unbalancedclassification is one of the primary issues with plagiarism detection. Wesuggest a focal loss-based training technique that carefully learns minorityclass instances to solve this. Another issue that we tackle is the trainingphase itself, which typically employs gradient-based methods likeback-propagation for the learning process and thus suffers from some drawbacks,including sensitivity to initialization. To initiate the BP process, we suggesta novel DE algorithm that makes use of a clustering-based mutation operator.Here, a winning cluster is identified for the current DE population, and afresh updating method is used to produce potential answers. We evaluate ourproposed approach on three benchmark datasets ( MSRP, SNLI, and SemEval2014)and demonstrate that it performs well when compared to both conventional andpopulation-based methods.

A Novel Plagiarism Detection Approach Combining BERT-based Word Embedding, Attention-based LSTMs and an Improved Differential Evolution Algorithm