Automated Plagiarism Detection Model Based On Deep Siamese Network
- Resource Type
- Conference
- Authors
- Zhang, Jing; Xue, Siyuan; Li, Jie Liu; She, Jian
- Source
- 2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Cloud Computing and Intelligent Systems (CCIS), 2022 IEEE 8th International Conference on. :298-302 Nov, 2022
- Subject
- Computing and Processing
Fuses
Plagiarism
Computational modeling
Semantics
Bit error rate
Education
Transformers
Plagiarism Detection
Natural Language Processing
Deep Siamese Network
Semantic similarity
- Language
This paper presents a novel deep Siamese network for automatic plagiarism detection. Our model utilizes a large-scale pre-trained model BERT (bidirectional encoder representations from transformers) to represent the text as word vector, and uses Bi-LSTM (bidirectional long short-term memory) net works to obtain the contextual semantic features of the text, and designs a text semantic interaction me chanism to obtain the interactive semantic features. Our model uses Siamese network to uniformly map matched text pairs into the same parameter matrix s pace. Meanwhile, our model uses multi-head self-attention to fuse text pair vectors for accurate semantic alignment and similarity measures. The experiment al results show that the effect of this model can identify and detect plagiarized text.