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.
@article{arxiv.2205.12495,
title = {ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection},
author = {Badr AlKhamissi and Faisal Ladhak and Srini Iyer and Ves Stoyanov and Zornitsa Kozareva and Xian Li and Pascale Fung and Lambert Mathias and Asli Celikyilmaz and Mona Diab},
journal= {arXiv preprint arXiv:2205.12495},
year = {2023}
}