LEMT: A Label Enhanced Multi-task Learning Framework for Malevolent Dialogue Response Detection

Kaiyue Wang, Fan Yang, Yucheng Yao, Xiabing Zhou

Abstract: Malevolent Dialogue Response Detection has gained much attention from the NLP community recently. Existing methods have difficulties in effectively utilizing the conversational context and the malevolent information. In this work, we propose a novel framework, the Label Enhanced Multi-task learning (LEMT), which incorporates a structured representation of malevolence description information and exploits malevolence shift detection as an auxiliary task. Specifically, we introduce a hierarchical structure encoder based on prior probability knowledge to capture the semantic information of different malevolent types and integrate it with utterance information. In addition, the malevolence shift detection is modeled to improve the ability of the model to distinguish between different malevolent information. Experimental results show that our LEMT outperforms state-of-the-art methods and verifies the effectiveness of the modules.