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Automatic hate speech detection is hampered by the scarcity of labeleddatasetd, leading to poor generalization. We employ pretrained language models(LMs) to alleviate this data bottleneck. We utilize the GPT LM for generatinglarge amounts of synthetic hate speech sequences from available labeledexamples, and leverage the generated data in fine-tuning large pretrained LMson hate detection. An empirical study using the models of BERT, RoBERTa andALBERT, shows that this approach improves generalization significantly andconsistently within and across data distributions. In fact, we find thatgenerating relevant labeled hate speech sequences is preferable to usingout-of-domain, and sometimes also within-domain, human-labeled examples.

Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech