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