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Textual Plagiarism Detection Using Embedding Models and Siamese LSTM

Plagiarism is a severe issue that puts academic integrity and intellectual property rights in trouble. Plagiarism occurs in a variety of ways in scientific institutes, the most common of which being direct plagiarism. Manually inspecting plagiarism activities in big volumes of texts is a difficult task. As a result, numerous detection algorithms have been proposed in order to avoid lengthy manual screening. In this paper, we describe a deep learning framework for detecting text plagiarism that uses a network of Siamese LSTM and embeddings based on word. The purpose of this study is to figure out which word-based embedding architecture yields the best accurate plagiarism detection results. To prepare input of the network, which must be multiple pre-trained sequences of text embedding, the proposed framework employs Word2Vec and Glove models. The ratio of plagiarism in the network's two outputs are then measured using a hybrid of Manhattan distance and cosine similarity measures. To the best of our knowledge, this is one of the few studies that attempts to detect plagiarism using several embedding models and hybrid similarity measures. The embeddings from the Word2Vec architecture produce the best accurate detection scores, according to experimental findings on the PAN-PC-11 and Webis-CPC-11 datasets. The method attained F1-measure of 0.816, recall of 0.91, and precision of 0.924for PAN-PC-11 corpus and F1-measure of 0.793, a recall of 0.852, and precision of 0.902for Webis-CPC-11 corpus using word2vec embeddings.