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