Publication Type
Conference Proceeding Article
Book Title/Conference/Journal
Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain, 2020 December 8-13
Year
12-2020
Abstract
Academia and industry have developed machine learning and natural language processing models to detect online hate speech automatically. However, most of these existing methods adopt a supervised approach that heavily depends on labeled datasets for training. This results in the methods’ poor detection performance of the hate speech class as the training datasets are highly imbalanced. In this paper, we propose HateGAN, a deep generative reinforcement learning model, which addresses the challenge of imbalance class by augmenting the dataset with hateful tweets. We conduct extensive experiments to augment two commonly-used hate speech detection datasets with the HateGAN generated tweets. Our experiment results show that HateGAN improves the detection performance of the hate speech class regardless of the classifiers and datasets used in the detection task. Specifically, we observe an average 5% improvement for the hate class F1 scores across all state-of-the-art hate speech classifiers. We also conduct case studies to empirically examine the HateGAN generated hate speeches and show that the generated tweets are diverse, coherent, and relevant to hate speech detection.
Disciplines
Computer Sciences
Subject(s)
Applied or Integration/Application Scholarship
Publisher
ACL
DOI
10.18653/v1/2020.coling-main.557
Version
publishedVersion
Language
eng
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Format
application/PDF
Citation
CAO, Rui and LEE, Roy Ka-Wei.
HateGAN: Adversarial generative-based data augmentation for hate speech detection. (2020). Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain, 2020 December 8-13. 6327-6338.
Available at: https://ink.library.smu.edu.sg/scis_studentpub/3
Additional URL
https://aclanthology.org/2020.coling-main.557/
