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As pointed out by several scholars, current research on hate speech (HS)recognition is characterized by unsystematic data creation strategies anddiverging annotation schemata. Subsequently, supervised-learning models tend togeneralize poorly to datasets they were not trained on, and the performance ofthe models trained on datasets labeled using different HS taxonomies cannot becompared. To ease this problem, we propose applying extremely weak supervisionthat only relies on the class name rather than on class samples from theannotated data. We demonstrate the effectiveness of a state-of-the-artweakly-supervised text classification model in various in-dataset andcross-dataset settings. Furthermore, we conduct an in-depth quantitative andqualitative analysis of the source of poor generalizability of HSclassification models.

Towards Weakly-Supervised Hate Speech Classification Across Datasets