论文标题
数据自举方法，以改善用于指示语言的低资源滥用语言检测
Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages
论文作者
论文摘要
在许多社交媒体平台上，虐待语言是日益关注的问题。反复接触滥用语音已对目标用户产生生理影响。因此，应以各种形式解决滥用语言的问题，以进行在线和平与安全。尽管滥用语音检测存在广泛的研究，但大多数研究都集中在英语上。最近，印度发生了许多污染事件，这些事件基于地理位置，以各种语言引起了各种形式的滥用言论。因此，处理这种恶意内容至关重要。在本文中，为了弥合差距，我们展示了对语言中多语言滥用语音的大规模分析。我们检查了不同的上峰转移机制，并观察到八种不同指示语言的滥用语音检测的各种多语言模型的性能。我们还试验以显示这些模型在对抗攻击中的鲁棒性。最后，我们通过研究模型在各种设置中的错误分类帖子来进行深入的错误分析。我们已将代码和模型公开为其他研究人员。
Abusive language is a growing concern in many social media platforms. Repeated exposure to abusive speech has created physiological effects on the target users. Thus, the problem of abusive language should be addressed in all forms for online peace and safety. While extensive research exists in abusive speech detection, most studies focus on English. Recently, many smearing incidents have occurred in India, which provoked diverse forms of abusive speech in online space in various languages based on the geographic location. Therefore it is essential to deal with such malicious content. In this paper, to bridge the gap, we demonstrate a large-scale analysis of multilingual abusive speech in Indic languages. We examine different interlingual transfer mechanisms and observe the performance of various multilingual models for abusive speech detection for eight different Indic languages. We also experiment to show how robust these models are on adversarial attacks. Finally, we conduct an in-depth error analysis by looking into the models' misclassified posts across various settings. We have made our code and models public for other researchers.
