November 30, 2022
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. ATOMIC) 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.
Written by
Badr Alkhamissy
Asli Celikyilmaz
Lambert Mathias
Mona Diab
Pascale Fung
Srini Iyer
Ves Stoyanov
Zornitsa Kozareva
Faisal Ladhak
Publisher
EMNLP
April 22, 2024
Vasu Sharma *, Karthik Padthe *, Newsha Ardalani, Kushal Tirumala, Russ Howes, Hu Xu, Bernie Huang, Daniel Li (FAIR), Armen Aghajanyan, Gargi Ghosh, Luke Zettlemoyer
April 22, 2024
April 14, 2024
Heng-Jui Chang, Ning Dong (AI), Ruslan Mavlyutov, Sravya Popuri, Andy Chung
April 14, 2024
April 05, 2024
Suyu Ge, Chunting Zhou, Rui Hou, Madian Khabsa, Yi-Chia Wang, Qifan Wang, Jiawei Han, Yuning Mao
April 05, 2024
February 21, 2024
Tom Sander, Pierre Fernandez, Alain Durmus, Matthijs Douze, Teddy Furon
February 21, 2024
Product experiences
Foundational models
Product experiences
Latest news
Foundational models