Aggressive Language Detection with Joint Text Normalization via Adversarial Multi-task Learning
This addresses the problem of detecting abusive language in social media texts for NLP applications, with an incremental improvement over existing methods.
The paper tackles aggressive language detection by jointly performing text normalization via an adversarial multi-task learning framework, resulting in outperforming all baselines on four datasets by large margins.
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.