论文标题
将仇恨语音检测扩展到资源不足的语言的数据有效策略
Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages
论文作者
论文摘要
仇恨言论是一种全球现象，但到目前为止，大多数仇恨言论数据集都集中在英语内容上。这阻碍了数百种语言在世界各地使用的数百种语言的发展。需要更多的数据，但是注释可恶的内容昂贵，耗时且可能对注释者有害。为了减轻这些问题，我们探讨了将仇恨语音检测扩展到资源不足的语言的数据有效策略。在跨五种非英语语言的单语和多语言模型进行的一系列实验中，我们发现1）需要少量目标语言微调数据来实现强大的性能，2）使用更多此类数据呈指数呈指数降低的好处； 3）最初对易于获取的英语数据的初步微调可以部分替代目标语言数据，并改善目标语言数据的模型通用性。根据这些发现，我们为低资源语言设置中的仇恨言论检测提出了可行的建议。
Hate speech is a global phenomenon, but most hate speech datasets so far focus on English-language 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, time-consuming and potentially harmful to annotators. To mitigate these issues, we explore data-efficient 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 target-language data and improve model generalisability. Based on these findings, we formulate actionable recommendations for hate speech detection in low-resource language settings.
