Lifelong Learning of Hate Speech Classification on Social Media

Jing Qian, Hong Wang, Mai ElSherief, Xifeng Yan


Abstract
Existing work on automated hate speech classification assumes that the dataset is fixed and the classes are pre-defined. However, the amount of data in social media increases every day, and the hot topics changes rapidly, requiring the classifiers to be able to continuously adapt to new data without forgetting the previously learned knowledge. This ability, referred to as lifelong learning, is crucial for the real-word application of hate speech classifiers in social media. In this work, we propose lifelong learning of hate speech classification on social media. To alleviate catastrophic forgetting, we propose to use Variational Representation Learning (VRL) along with a memory module based on LB-SOINN (Load-Balancing Self-Organizing Incremental Neural Network). Experimentally, we show that combining variational representation learning and the LB-SOINN memory module achieves better performance than the commonly-used lifelong learning techniques.
Anthology ID:
2021.naacl-main.183
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2304–2314
Language:
URL:
https://aclanthology.org/2021.naacl-main.183
DOI:
10.18653/v1/2021.naacl-main.183
Bibkey:
Cite (ACL):
Jing Qian, Hong Wang, Mai ElSherief, and Xifeng Yan. 2021. Lifelong Learning of Hate Speech Classification on Social Media. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2304–2314, Online. Association for Computational Linguistics.
Cite (Informal):
Lifelong Learning of Hate Speech Classification on Social Media (Qian et al., NAACL 2021)
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PDF:
https://aclanthology.org/2021.naacl-main.183.pdf
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