  Standard approaches to hate speech detection rely on sufficient available
hate speech annotations. Extending previous work that repurposes natural
language inference (NLI) models for zero-shot text classification, we propose a
simple approach that combines multiple hypotheses to improve English NLI-based
zero-shot hate speech detection. We first conduct an error analysis for vanilla
NLI-based zero-shot hate speech detection and then develop four strategies
based on this analysis. The strategies use multiple hypotheses to predict
various aspects of an input text and combine these predictions into a final
verdict. We find that the zero-shot baseline used for the initial error
analysis already outperforms commercial systems and fine-tuned BERT-based hate
speech detection models on HateCheck. The combination of the proposed
strategies further increases the zero-shot accuracy of 79.4% on HateCheck by
7.9 percentage points (pp), and the accuracy of 69.6% on ETHOS by 10.0pp.
