面向豪萨语、约鲁巴语和伊博语的攻击性语言与仇恨言论检测多语言数据集
A multilingual dataset for offensive language and hate speech detection for hausa, yoruba and igbo languages
AI总结：
本文构建了豪萨语、约鲁巴语和伊博语的攻击性语言检测数据集，并用预训练语言模型验证其有效性，最佳模型准确率达90%。
摘要：
网络攻击性语言的泛滥使得开发有效的检测机制成为必要，尤其是在多语言环境中。本研究通过开发并引入三个主要尼日利亚语言——豪萨语、约鲁巴语和伊博语——的攻击性语言检测新数据集来应对这一挑战。我们从 Twitter 收集数据，并由母语者进行人工标注，为这三种语言分别构建了数据集。我们使用预训练语言模型评估其在我们的数据集上检测攻击性语言的效能。表现最佳的模型达到了 90% 的准确率。为了进一步支持攻击性语言检测研究，我们计划将数据集和模型公开提供。
英文摘要：
The proliferation of online offensive language necessitates the development of effective detection mechanisms, especially in multilingual contexts. This study addresses the challenge by developing and introducing novel datasets for offensive language detection in three major Nigerian languages: Hausa, Yoruba, and Igbo. We collected data from Twitter and manually annotated it to create datasets for each of the three languages, using native speakers. We used pre-trained language models to evaluate their efficacy in detecting offensive language in our datasets. The best-performing model achieved an accuracy of 90\%. To further support research in offensive language detection, we plan to make the dataset and our models publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
