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Online Active Learning for Offensive Language Detection

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DOI: 10.23977/acss.2020.040104 | Downloads: 11 | Views: 335

Author(s)

Yixuan Chai 1, Guohua Liu 1

Affiliation(s)

1 School of Computer Science and Technology, Donghua University, Shanghai, China

Corresponding Author

Yixuan Chai

ABSTRACT

Recent success in deep learning-based offensive language detection, however, the off-line learning strategy cannot cope with the challenge of rapid growth and evolution of offensive language. In this paper, we introduce an online active learning method to offensive language detection model. Online learning makes use of the user’s report feedbacks to continue training the detection model. In addition, active learning methods can filter out the mislabeled feedbacks to ensure the safety of the online learning process. Extensive experiments demonstrate that the proposed method can achieve promising results on the offensive response dataset.

KEYWORDS

Offensive language detection, Online learning, Active learning

CITE THIS PAPER

Yixuan Chai, and Guohua Liu. Online Active Learning for Offensive Language Detection. Advances in Computer, Signals and Systems (2020) 4: 19-24. DOI: http://dx.doi.org/10.23977/acss.2020.040104.

REFERENCES

[1] M. Dadvar, R. Ordelman, F. De Jong, and D. Trieschnigg, “Improved cyberbullying detection using gender information,” in Dutch-Belgian Information Retrieval Workshop, DIR 2012, 2012, pp. 23–26.
[2] E. Greevy and A. F. Smeaton, “Classifying racist texts using a support vector machine,” in Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2004, pp. 468–469.
[3] M. Dadvar, D. Trieschnigg, and F. De Jong, “Expert knowledge for automatic detection of bullies in social networks,” in Belgian/Netherlands Artificial Intelligence Conference, 2013, pp. 57–63.
[4] Y. Chen, “DETECTING OFFENSIVE LANGUAGE IN SOCIAL MEDIAS FOR PROTECTION OF by,” no. December, 2011.
[5] J. Li, Y. Song, C. Gao, M. R. Lyu, and I. King, “Topic Memory Networks for Short Text Classification,” arXiv Prepr., vol. arXiv:1809, 2018.
[6] A. Cimino and F. Dell’Orletta, “Hate Me, Hate Me Not: Hate Speech Detection on Facebook,” 2017.
[7] A. Zhang, B. Li, S. Wan, and K. Wang, “Cyberbullying Detection with BiRNN and Attention Mechanism,” in Machine Learning and Intelligent Communications, 2019, pp. 623–635.
[8] D. D. Lewis and W. A. Gale, “A sequential algorithm for training text classifiers,” Proc. 17th Annu. Int. ACM SIGIR Conf. Res. Dev. Inf. Retrieval, SIGIR 1994, vol. 29, no. 2, pp. 3–12, 1994.
[9] H. S. Seung, M. Opper, and H. Sompolinsky, “Query by committee,” in Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, 1992, pp. 287–294.
[10] M. C. Kenton, L. Kristina, and J. Devlin, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” 2017.
[11] https://github.com/chaiyixuan/Offensive-Responses-Dataset
[12] https://github.com/chaiyixuan/onlineAL

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