Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages
This work addresses the gap in abusive speech detection for Indic languages, which is crucial for online safety in India, but it is incremental as it applies existing methods to new data.
The paper tackles the problem of abusive language detection for low-resource Indic languages by analyzing multilingual models and their robustness to adversarial attacks, achieving performance evaluations across eight languages.
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.