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Enhancing Offensive Language Detection with Data Augmentation and Knowledge Distillation.

Jiawen Deng1,1, Zhuang Chen1, Hao Sun1

  • 1The CoAI group, DCST; Institute for Artificial Intelligence; State Key Lab of Intelligent Technology and Systems; Beijing National Research Center for Information Science and Technology; Tsinghua University, Beijing 100084, China.

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This study introduces AugCOLD, a 1 million sample dataset to improve Chinese offensive language detection. A novel multiteacher distillation framework enhances model performance and robustness for safer online communication.

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Offensive language detection is vital for social media and safe AI deployment.
  • Existing Chinese datasets for offensive language are limited in scale and scope compared to English resources.
  • This data scarcity hinders the accuracy of Chinese offensive language detectors, particularly for complex or novel cases.

Purpose of the Study:

  • To address the limitations of existing Chinese offensive language datasets.
  • To develop a large-scale, unsupervised dataset for training more robust detectors.
  • To enhance the performance and generalization capabilities of Chinese offensive language detection models.

Main Methods:

  • Introduced AugCOLD (Augmented Chinese Offensive Language Dataset), a 1 million sample unsupervised dataset created via data crawling and model generation.
  • Employed a multiteacher knowledge distillation framework to leverage unsupervised data.
  • Utilized publicly available datasets to train multiple teacher models, which then assigned soft labels to AugCOLD for knowledge transfer to a student network (the final detector).

Main Results:

  • Demonstrated significant improvements in offensive language detection performance.
  • Showcased enhanced generalization and robustness of the offensive language detector on various test sets, including challenging hard cases.
  • Validated the effectiveness of the proposed multiteacher distillation approach with the AugCOLD dataset.

Conclusions:

  • The AugCOLD dataset and the multiteacher distillation framework effectively address the scarcity of Chinese offensive language data.
  • The proposed method significantly improves the accuracy, generalization, and robustness of Chinese offensive language detectors.
  • This work contributes to safer online communication and the responsible deployment of large language models in Chinese contexts.