Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages

Hate speech is a global phenomenon, but most hate speech datasets so far focus on Englishlanguage content. This hinders the development of more effective hate speech detection models in hundreds of languages spoken by billions across the world. More data is needed, but annotating hateful content is expensive, timeconsuming and potentially harmful to annotators. To mitigate these issues, we explore dataefficient strategies for expanding hate speech detection into under-resourced languages. In a series of experiments with mono-and multilingual models across five non-English languages, we find that 1) a small amount of target-language fine-tuning data is needed to achieve strong performance, 2) the benefits of using more such data decrease exponentially, and 3) initial fine-tuning on readily-available English data can partially substitute targetlanguage data and improve model generalisability. Based on these findings, we formulate actionable recommendations for hate speech detection in low-resource language settings. Content warning: This article contains illustrative examples of hateful language.