TAMA: Target-Aware Multilingual Abuse Detection by Cascaded Conditional Multi-Task Learning
JiYan Liu Qingdao University of Science and Technology, Youzheng Liu, Taihang Wang, Yimin Wang Qingdao University of Science and Technology, Ye Jiang Qingdao University of Science and Technology, Diana Maynard University of Sheffield
Protecting public figures from online abuse requires models that go beyond post-level classification to determine whether abuse is directed at a designated target, characterize the abuse intent, and extract textual evidence. We introduce a Target-Aware Multilingual Abuse (TAMA), benchmark of 9,386 X (Twitter) posts aimed at public figures, with aligned supervision for (i) tri-class target detection, (ii) 12-way fine-grained abuse type classification, and (iii) phrase-level abusive spans localization. To exploit the hierarchical coupling of these tasks, we propose Cascaded-MTL, a dependency-aware multi-task framework that conditions downstream predictions on upstream beliefs via three lightweight modules: Cross-Task Feature Fusion (CTF), Task-Adaptive Gating (TAG), and Label-Guided Span Detection (LGSD). Experiments across three multilingual encoders show that Cascaded-MTL consistently yields higher average F1 than single-task and standard multi-task training and delivers robust gains on type classification and span localization. The code and the dataset are released here: https://github.com/zgjiangtoby/CASCADED-MTL
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表