  Hate speech detection is complex; it relies on commonsense reasoning,
knowledge of stereotypes, and an understanding of social nuance that differs
from one culture to the next. It is also difficult to collect a large-scale
hate speech annotated dataset. In this work, we frame this problem as a
few-shot learning task, and show significant gains with decomposing the task
into its "constituent" parts. In addition, we see that infusing knowledge from
reasoning datasets (e.g. Atomic2020) improves the performance even further.
Moreover, we observe that the trained models generalize to out-of-distribution
datasets, showing the superiority of task decomposition and knowledge infusion
compared to previously used methods. Concretely, our method outperforms the
baseline by 17.83% absolute gain in the 16-shot case.
