A Rationale-Guided Multi-Task Learning Framework for Hate Speech Detection

Hate Speech Detection (HSD) plays a crucial role in ensuring respectful online communication and preventing the spread of harmful content. However, existing studies on HSD often focus on the overall intent of the target message while overlooking the fine-grained details in the message. We propose a RatIonale-guided multi-taSk lEarning framework (RISE), which frames HSD as the main task and Human Rationales Tagging (HRT) as the auxiliary task. This enables simultaneous message-level understanding and the identification of expressions that trigger human judgments of hate speech, mimicking human cognitive processing to capture fine-grained information and enhance HSD performance. Furthermore, we extend rationale annotations from binary labels to BIO tagging, capturing the positional roles of tokens within rationales. Additionally, we integrate an emoji semantics interpretation module that interprets emoji meanings, enriching contextual information. Extensive experiments demonstrate that RISE outperforms state-of-the-art models, with each component contributing significantly to improved performance.