Social media often serves as a breeding ground for various hateful and offensive content. Identifying such content on social media is crucial due to its impact on the race, gender, or religion in an unprejudiced society. However, while there is extensive research in hate speech detection in English, there is a gap in hateful content detection in low-resource languages like Bengali. Besides, a current trend on social media is the use of Romanized Bengali for regular interactions. To overcome the existing research's limitations, in this study, we develop an annotated dataset of 10K Bengali posts consisting of 5K actual and 5K Romanized Bengali tweets. We implement several baseline models for the classification of such hateful posts. We further explore the interlingual transfer mechanism to boost classification performance. Finally, we perform an in-depth error analysis by looking into the misclassified posts by the models. While training actual and Romanized datasets separately, we observe that XLM-Roberta performs the best. Further, we witness that on joint training and few-shot training, MuRIL outperforms other models by interpreting the semantic expressions better. We make our code and dataset public for others.

本研究针对Bengali等低资源语言在社交媒体上的仇恨言论进行研究，开发了一个定量数据集并建立了基线模型和一些额外的方法以更好地进行分类。在分析误分类之后，我们确认XLM-Roberta在单独训练actual或rominized数据集时表现最好。而在联合训练和小样本训练中，MuRIL通过更好地解析语义表达比其他模型表现更优秀。

孟加拉语仇恨言论和冒犯性语言检测