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

Badr AlKhamissi, Faisal Ladhak, Srinivasan Iyer, Veselin Stoyanov, Zornitsa Kozareva, Xian Li, Pascale Fung, Lambert Mathias, Asli Celikyilmaz, Mona Diab

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.
Anthology ID:
2022.emnlp-main.136
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2109–2120
Language:
URL:
https://aclanthology.org/2022.emnlp-main.136
DOI:
10.18653/v1/2022.emnlp-main.136
Bibkey:
Cite (ACL):
Badr AlKhamissi, Faisal Ladhak, Srinivasan Iyer, Veselin Stoyanov, Zornitsa Kozareva, Xian Li, Pascale Fung, Lambert Mathias, Asli Celikyilmaz, and Mona Diab. 2022. ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2109–2120, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection (AlKhamissi et al., EMNLP 2022)
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