  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.
