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PDMTT: A Plagiarism Detection Model Towards Multi-turn Text Back-Translation

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Digital Forensics and Watermarking (IWDW 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14511))

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Abstract

With the development of communication technologies, the practice of creating new texts by manipulating original sentence structures through multi-turn machine translation is widespread across various domains. Existing plagiarism detection models often treat different features uniformly and overlook the significance of disparities within high-dimensional features. Therefore, this paper proposes a novel plagiarism detection model towards multi-turn text back-translation (PDMTT), adopting a novel mechanism that combines local and global features and enhances them. The grouping enhancement fusion (GEF) mechanism assigns importance coefficients to sub-features, reinforcing critical aspects while diminishing less relevant ones. These enhanced features, generated by the GEF mechanism, are leveraged to extract high-quality text representations, thereby improving the precision of the model in distinguishing original content from back-translated texts. Furthermore, we improve the back-translation plagiarism detection capability of our model by optimizing the contrastive loss function and utilizing the fused translated representations as targets. To validate the effectiveness of our model, we also constructed a multi-tuple back-translation plagiarism dataset for model training and validation. Experimental results demonstrate that the proposed PDMTT outperforms previous methods in back-translation plagiarism detection, yielding superior text representations. The ablation study further confirms that the incorporation of the GEF mechanism effectively enhances the discrimination capability of our model.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants U20B2051 and 62172280; in part by the Natural Science Foundation of Shanghai under Grants 21ZR1444600; and in part by the Shanghai Science and Technology Committee Capability Construction Project for Shanghai Municipal Universities under Grant 20060502300.

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Authors and Affiliations

  1. University of Shanghai for Science and Technology, Shanghai, China

    Xiaoling He, Yuanding Zhou & Chuan Qin

  2. Fudan University, Shanghai, China

    Zhenxing Qian & Xinpeng Zhang

Authors
  1. Xiaoling He
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  2. Yuanding Zhou
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  3. Chuan Qin
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  4. Zhenxing Qian
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  5. Xinpeng Zhang
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Corresponding author

Correspondence to Chuan Qin .

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Editors and Affiliations

  1. School of Cyber Security, Qilu University of Technology, Jinan, China

    Bin Ma

  2. Qilu University of Technology, Jinan, China

    Jian Li

  3. Qilu University of Technology, Jinan, China

    Qi Li

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He, X., Zhou, Y., Qin, C., Qian, Z., Zhang, X. (2024). PDMTT: A Plagiarism Detection Model Towards Multi-turn Text Back-Translation. In: Ma, B., Li, J., Li, Q. (eds) Digital Forensics and Watermarking. IWDW 2023. Lecture Notes in Computer Science, vol 14511. Springer, Singapore. https://doi.org/10.1007/978-981-97-2585-4_6

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  • DOI: https://doi.org/10.1007/978-981-97-2585-4_6

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  • Print ISBN: 978-981-97-2584-7

  • Online ISBN: 978-981-97-2585-4

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