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Hate speech classification has been a long-standing problem in naturallanguage processing. However, even though there are numerous hate speechdetection methods, they usually overlook a lot of hateful statements due tothem being implicit in nature. Developing datasets to aid in the task ofimplicit hate speech classification comes with its own challenges; difficultiesare nuances in language, varying definitions of what constitutes hate speech,and the labor-intensive process of annotating such data. This had led to ascarcity of data available to train and test such systems, which gives rise tohigh variance problems when parameter-heavy transformer-based models are usedto address the problem. In this paper, we explore various optimization andregularization techniques and develop a novel RoBERTa-based model that achievesstate-of-the-art performance.

Combating high variance in Data-Scarce Implicit Hate Speech Classification