Skip to main content

Aggressive Language Detection with Joint Text Normalization via Adversarial Multi-task Learning

  • Conference paper
  • First Online:
Natural Language Processing and Chinese Computing (NLPCC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12430))

Abstract

Aggressive language detection (ALD), detecting the abusive and offensive language in texts, is one of the crucial applications in NLP community. Most existing works treat ALD as regular classification with neural models, while ignoring the inherent conflicts of social media text that they are quite unnormalized and irregular. In this work, we target improving the ALD by jointly performing text normalization (TN), via an adversarial multi-task learning framework. The private encoders for ALD and TN focus on the task-specific features retrieving, respectively, and the shared encoder learns the underlying common features over two tasks. During adversarial training, a task discriminator distinguishes the separate learning of ALD or TN. Experimental results on four ALD datasets show that our model outperforms all baselines under differing settings by large margins, demonstrating the necessity of joint learning the TN with ALD. Further analysis is conducted for a better understanding of our method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://sites.google.com/view/trac1/home.

  2. 2.

    https://github.com/t-davidson/hate-speech-and-offensive-language.

  3. 3.

    https://competitions.codalab.org/competitions/20011.

  4. 4.

    https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge.

  5. 5.

    https://noisy-text.github.io/2015/index.html.

  6. 6.

    A variant of macro F-score that takes into consideration the instance numbers for each label. It can result in a value that is not between precision and recall.

  7. 7.

    https://allennlp.org/elmo.

  8. 8.

    https://nlp.stanford.edu/projects/glove/.

  9. 9.

    https://github.com/google-research/bert, base-cased-version.

  10. 10.

    https://github.com/cbaziotis/ekphrasis.

References

  1. Badjatiya, P., Gupta, S., Gupta, M., Varma, V.: Deep learning for hate speech detection in tweets. In: Proceedings of the International Conference on World Wide Web Companion, pp. 759–760 (2017)

    Google Scholar 

  2. Baldwin, T., de Marneffe, M.C., Han, B., Kim, Y.B., Ritter, A., Xu, W.: Shared tasks of the 2015 workshop on noisy user-generated text: twitter lexical normalization and named entity recognition. In: Proceedings of the Workshop on Noisy User-generated Text, pp. 126–135 (2015)

    Google Scholar 

  3. Brassard-Gourdeau, E., Khoury, R.: Subversive toxicity detection using sentiment information. In: Proceedings of the Workshop on Abusive Language Online, pp. 1–10 (2019)

    Google Scholar 

  4. Cimino, A., De Mattei, L., Dell’Orletta, F.: Multi-task learning in deep neural networks at evalita 2018. In: Proceedings of the Wvaluation Campaign of Natural Language Processing and Speech tools for Italian, pp. 86–95 (2018)

    Google Scholar 

  5. Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language. In: Proceedings of International Conference on Web and Social Media, (2017)

    Google Scholar 

  6. Dehghani, M., Gouws, S., Vinyals, O., Uszkoreit, J., Kaiser, Ł.: Universal transformers. arXiv preprint arXiv:1807.03819 (2018)

  7. Fei, H., Ji, D., Zhang, Y., Ren, Y.: Topic-enhanced capsule network for multi-label emotion classification. IEEE/ACM Trans. Audio Speech Lang. Process. 28, 1839–1848 (2020)

    Article  Google Scholar 

  8. Fei, H., Ren, Y., Ji, D.: Implicit objective network for emotion detection. In: Proceedings of the NLPCC, pp. 647–659 (2019)

    Google Scholar 

  9. Fei, H., Ren, Y., Ji, D.: Boundaries and edges rethinking: an end-to-end neural model for overlapping entity relation extraction. Inf. Process. Manage. 57(6), 102311 (2020)

    Article  Google Scholar 

  10. Fei, H., Ren, Y., Ji, D.: Dispatched attention with multi-task learning for nested mention recognition. Inf. Sci. 513, 241–251 (2020)

    Article  Google Scholar 

  11. Fei, H., Zhang, M., Ji, D.: Cross-lingual semantic role labeling with high-quality translated training corpus. In: Proceedings of the ACL, pp. 7014–7026 (2020)

    Google Scholar 

  12. Fei, H., Zhang, Y., Ren, Y., Ji, D.: Latent emotion memory for multi-label emotion classification. In: Proceedings of the AAAI, pp. 7692–7699 (2020)

    Google Scholar 

  13. Gambäck, B., Sikdar, U.K.: Using convolutional neural networks to classify hate-speech. In: Proceedings of the Workshop on Abusive Language Online, pp. 85–90 (2017)

    Google Scholar 

  14. Gao, S., Ramanathan, A., Tourassi, G.: Hierarchical convolutional attention networks for text classification. In: Proceedings of Workshop on Representation Learning for NLP, pp. 11–23 (2018)

    Google Scholar 

  15. Hassan, H., Menezes, A.: Social text normalization using contextual graph random walks. In: Proceedings of the EMNLP, pp. 1577–1586 (2013)

    Google Scholar 

  16. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  17. Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)

  18. Kumar, R., Ojha, A.K., Malmasi, S., Zampieri, M.: Benchmarking aggression identification in social media. In: Proceedings of the Workshop on Trolling, Aggression and Cyberbullying, pp. 1–11 (2018)

    Google Scholar 

  19. Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Proceedings of AAAI, (2015)

    Google Scholar 

  20. Lal, Y.K., Kumar, V., Dhar, M., Shrivastava, M., Koehn, P.: De-mixing sentiment from code-mixed text. In: Proceedings of the ACL, pp. 371–377 (2019)

    Google Scholar 

  21. Liu, P., Qiu, X., Huang, X.: Recurrent neural network for text classification with multi-task learning. arXiv preprint arXiv:1605.05101 (2016)

  22. Liu, P., Qiu, X., Huang, X.: Adversarial multi-task learning for text classification. In: Proceedings of the ACL, pp. 1–10 (2017)

    Google Scholar 

  23. Madisetty, S., Desarkar, M.S.: Aggression detection in social media using deep neural networks. In: Proceedings of the Workshop on Trolling, Aggression and Cyberbullying, pp. 120–127 (2018)

    Google Scholar 

  24. Nikhil, N., Pahwa, R., Nirala, M.K., Khilnani, R.: Lstms with attention for aggression detection. In: Proceedings of the Workshop on Trolling, Aggression and Cyberbullying, pp. 52–57 (2018)

    Google Scholar 

  25. Peters, M.E., et al.: Deep contextualized word representations. arXiv preprint arXiv:1802.05365 (2018)

  26. Ramiandrisoa, F., Mothe, J.: Irit at trac 2018. In: Proceedings of the Workshop on Trolling, Aggression and Cyberbullying, pp. 19–27 (2018)

    Google Scholar 

  27. Schmidt, A., Wiegand, M.: A survey on hate speech detection using natural language processing. In: Proceedings of the International Workshop on Natural Language Processing for Social Media, pp. 1–10 (2017)

    Google Scholar 

  28. Vaidya, A., Mai, F., Ning, Y.: Empirical analysis of multi-task learning for reducing model bias in toxic comment detection. arXiv preprint arXiv:1909.09758 (2019)

  29. Vaswani, A., et al.: Attention is all you need. In: Proceedings of the NeurIPS, pp. 5998–6008 (2017)

    Google Scholar 

  30. Wulczyn, E., Thain, N., Dixon, L.: Ex machina: personal attacks seen at scale. In: Proceedings of the International Conference on World Wide Web, pp. 1391–1399 (2017)

    Google Scholar 

  31. Yang, Y., Eisenstein, J.: A log-linear model for unsupervised text normalization. In: Proceedings of the ACL, pp. 61–72 (2013)

    Google Scholar 

  32. Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N., Kumar, R.: SemEval-2019 task 6: identifying and categorizing offensive language in social media (OffensEval). In: Proceedings of the International Workshop on Semantic Evaluation, pp. 75–86 (2019)

    Google Scholar 

  33. Zhang, Z., Robinson, D., Tepper, J.: Detecting hate speech on twitter using a convolution-gru based deep neural network. In: Proceedings of European Semantic Web Conference, pp. 745–760 (2018)

    Google Scholar 

  34. Zhou, J.T., et al.: Dual adversarial neural transfer for low-resource named entity recognition. In: Proceedings of the ACL, pp. 3461–3471 (2019)

    Google Scholar 

Download references

Acknowledgment

This work is supported by the National Natural Science Foundation of China (No. 61772378), the National Key Research and Development Program of China (No. 2017YFC1200500), the Humanities-Society Scientific Research Program of Ministry of Education (No. 20YJA740062), the Research Foundation of Ministry of Education of China (No. 18JZD015), and the Major Projects of the National Social Science Foundation of China (No. 11&ZD189).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Donghong Ji .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wu, S., Fei, H., Ji, D. (2020). Aggressive Language Detection with Joint Text Normalization via Adversarial Multi-task Learning. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12430. Springer, Cham. https://doi.org/10.1007/978-3-030-60450-9_54

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-60450-9_54

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60449-3

  • Online ISBN: 978-3-030-60450-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics