Most research on hate speech detection has focused on English where a sizeable amount of labeled training data is available. However, to expand hate speech detection into more languages, approaches that require minimal training data are needed. In this paper, we test whether natural language inference (NLI) models which perform well in zero- and few-shot settings can benefit hate speech detection performance in scenarios where only a limited amount of labeled data is available in the target language. Our evaluation on five languages demonstrates large performance improvements of NLI fine-tuning over direct fine-tuning in the target language. However, the effectiveness of previous work that proposed intermediate fine-tuning on English data is hard to match. Only in settings where the English training data does not match the test domain, can our customised NLI-formulation outperform intermediate fine-tuning on English. Based on our extensive experiments, we propose a set of recommendations for hate speech detection in languages where minimal labeled training data is available.

翻译：大多数关于仇恨言论检测的研究聚焦于英语，因为英语中存在大量可用的标注训练数据。然而，为了将仇恨言论检测扩展到更多语言，需要采用仅需最少训练数据的方法。在本文中，我们测试了在零样本和少样本设置下表现良好的自然语言推理（NLI）模型，是否能在目标语言仅有有限标注数据的情况下提升仇恨言论检测性能。我们在五种语言上的评估显示，与直接在目标语言上进行微调相比，NLI微调带来了显著的性能提升。然而，先前工作中提出的在英语数据上进行中间微调的方法的效果难以匹敌。仅当英语训练数据与测试领域不匹配时，我们定制的NLI公式才能超越在英语上的中间微调。基于广泛的实验，我们为标注训练数据极少的语言的仇恨言论检测提出了一系列建议。