Efficient reinforcement learning-based method for plagiarism detection boosted by a population-based algorithm for pretraining weights
Plagiarism detection (PD) in natural language processing involves locating similar words in two distinct sources. The paper introduces a new approach to plagiarism detection utilizing bidirectional encoder representations from transformers (BERT)-generated embedding, an enhanced artificial bee colon...
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| Vydáno v: | Expert systems with applications Ročník 238; s. 122088 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
15.03.2024
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | Plagiarism detection (PD) in natural language processing involves locating similar words in two distinct sources. The paper introduces a new approach to plagiarism detection utilizing bidirectional encoder representations from transformers (BERT)-generated embedding, an enhanced artificial bee colony (ABC) optimization algorithm for pre-training, and a training process based on reinforcement learning (RL). The BERT model can be incorporated into a subsequent task and meticulously refined to function as a model, enabling it to apprehend a variety of linguistic characteristics. Imbalanced classification is one of the fundamental obstacles to PD. To handle this predicament, we present a novel methodology utilizing RL, in which the problem is framed as a series of sequential decisions in which an agent receives a reward at each level for classifying a received instance. To address the disparity between classes, it is determined that the majority class will receive a lower reward than the minority class. We also focus on the training stage, which often utilizes gradient-based learning techniques like backpropagation (BP), leading to certain drawbacks such as sensitivity to initialization. In our proposed model, we utilize a mutual learning-based ABC (ML-ABC) approach that adjusts the food source with the most beneficial results for the candidate by considering a mutual learning factor that incorporates the initial weight. We evaluated the efficacy of our novel approach by contrasting its results with those of population-based techniques using three standard datasets, namely Stanford Natural Language Inference (SNLI), Microsoft Research Paraphrase Corpus (MSRP), and Semantic Evaluation Database (SemEval2014). Our model attained excellent results that outperformed state-of-the-art models. Optimal values for important parameters, including reward function are identified for the model based on experiments on the study dataset. Ablation studies that exclude the proposed ML-ABC and reinforcement learning from the model confirm the independent positive incremental impact of these components on model performance.
•BERT-based plagiarism detection with RL and ML-ABC.•Reward function in RL improves detection of minority plagiarism class.•Model outperforms state-of-the-art on SNLI, MSRP, SemEval2014 datasets.•Ablation studies highlight the impact of ML-ABC and RL on performance. |
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| AbstractList | Plagiarism detection (PD) in natural language processing involves locating similar words in two distinct sources. The paper introduces a new approach to plagiarism detection utilizing bidirectional encoder representations from transformers (BERT)-generated embedding, an enhanced artificial bee colony (ABC) optimization algorithm for pre-training, and a training process based on reinforcement learning (RL). The BERT model can be incorporated into a subsequent task and meticulously refined to function as a model, enabling it to apprehend a variety of linguistic characteristics. Imbalanced classification is one of the fundamental obstacles to PD. To handle this predicament, we present a novel methodology utilizing RL, in which the problem is framed as a series of sequential decisions in which an agent receives a reward at each level for classifying a received instance. To address the disparity between classes, it is determined that the majority class will receive a lower reward than the minority class. We also focus on the training stage, which often utilizes gradient-based learning techniques like backpropagation (BP), leading to certain drawbacks such as sensitivity to initialization. In our proposed model, we utilize a mutual learning-based ABC (ML-ABC) approach that adjusts the food source with the most beneficial results for the candidate by considering a mutual learning factor that incorporates the initial weight. We evaluated the efficacy of our novel approach by contrasting its results with those of population-based techniques using three standard datasets, namely Stanford Natural Language Inference (SNLI), Microsoft Research Paraphrase Corpus (MSRP), and Semantic Evaluation Database (SemEval2014). Our model attained excellent results that outperformed state-of-the-art models. Optimal values for important parameters, including reward function are identified for the model based on experiments on the study dataset. Ablation studies that exclude the proposed ML-ABC and reinforcement learning from the model confirm the independent positive incremental impact of these components on model performance.
•BERT-based plagiarism detection with RL and ML-ABC.•Reward function in RL improves detection of minority plagiarism class.•Model outperforms state-of-the-art on SNLI, MSRP, SemEval2014 datasets.•Ablation studies highlight the impact of ML-ABC and RL on performance. |
| ArticleNumber | 122088 |
| Author | Yan, Lei Alizadehsani, Roohallah Khan, Abdullah Ayub Xiong, Jiale Yang, Jing Acharya, U. Rajendra Awais, Muhammad |
| Author_xml | – sequence: 1 givenname: Jiale orcidid: 0000-0002-9075-4425 surname: Xiong fullname: Xiong, Jiale email: xjl747568988@gmail.com organization: Faculty of Computer Science and Information Technology, University Malaya, 50603 Kuala Lumpur, Malaysia – sequence: 2 givenname: Jing orcidid: 0009-0008-0467-5984 surname: Yang fullname: Yang, Jing email: s2147529@siswa.um.edu.my organization: Faculty of Computer Science and Information Technology, University Malaya, 50603 Kuala Lumpur, Malaysia – sequence: 3 givenname: Lei surname: Yan fullname: Yan, Lei email: yan1554684981@163.com organization: Faculty of Computer Science and Information Technology, University Malaya, 50603 Kuala Lumpur, Malaysia – sequence: 4 givenname: Muhammad orcidid: 0000-0003-1839-1356 surname: Awais fullname: Awais, Muhammad email: muhammadawais95@gmail.com organization: Department of Creative Technologies, Air University Islamabad, Pakistan – sequence: 5 givenname: Abdullah Ayub orcidid: 0000-0003-2838-7641 surname: Khan fullname: Khan, Abdullah Ayub email: abdullah.ayub@bbsul.edu.pk organization: Department of Computer Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi 75660, Pakistan – sequence: 6 givenname: Roohallah surname: Alizadehsani fullname: Alizadehsani, Roohallah email: r.alizadehsani@deakin.edu.au organization: Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University, Waurn Ponds, Australia – sequence: 7 givenname: U. Rajendra surname: Acharya fullname: Acharya, U. Rajendra email: aru@np.edu.sg organization: School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia |
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| Keywords | Bidirectional encoder representations from transformers Artificial bee colony Unbalanced classification Reinforcement learning Plagiarism detection |
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