基于对抗性多任务学习的联合文本规范化攻击性语言检测
Aggressive Language Detection with Joint Text Normalization via Adversarial Multi-task Learning
摘要：
攻击性语言检测（ALD）是自然语言处理领域的重要应用之一。现有的研究大多将ALD视为神经模型的规则分类，而忽略了社交媒体文本的内在冲突，它们非常不规范和不规则。在这项工作中，我们的目标是通过对抗性多任务学习框架联合执行文本规范化（TN）来改进ALD。ALD和TN的私有编码器分别专注于特定于任务的特征检索，而共享编码器学习两个任务的底层公共特征。在对抗性训练中，任务学习区分ALD或TN的单独学习。在四个ALD数据集上的实验结果表明，我们的模型在不同设置下的性能优于所有基线，证明了联合学习TN与ALD的必要性。为了更好地理解我们的方法，进行了进一步的分析。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
