Chain of Explanation: New Prompting Method to Generate Higher Quality Natural Language Explanation for Implicit Hate Speech

Recent studies have exploited advanced generative language models to generate Natural Language Explanations (NLE) for why a certain text could be hateful. We propose the Chain of Explanation (CoE) Prompting method, using the target group and retrieved social norms, to generate high-quality NLE for implicit hate speech. Providing accurate target information and high-quality related social norms, we improved the BLUE score from 44.0 to 62.3 for NLE generation. We then evaluate the quality of generated NLE from various automatic metrics and human annotations of informativeness and clarity scores. The correlation analysis between auto-metrics and human perceptions reveals insights into how to select suitable automatic metrics for Natural Language Generation tasks. To showcase a potential application of our proposed CoE method, we demonstrate the f1-score improvements from 0.635 to 0.655 for the implicit hate speech classification task.