ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection

• URL: http://arxiv.org/abs/2205.12495v2
• Date: Sat, 20 May 2023 17:11:44 GMT
• Title: ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection
• Authors: Badr AlKhamissi, Faisal Ladhak, Srini Iyer, Ves Stoyanov, Zornitsa Kozareva, Xian Li, Pascale Fung, Lambert Mathias, Asli Celikyilmaz, Mona Diab
• Abstract summary: 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. Atomic 2020) improves the performance even further.
• Score: 85.68684067031909
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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