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Social media often serves as a breeding ground for various hateful andoffensive content. Identifying such content on social media is crucial due toits 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 likeBengali. Besides, a current trend on social media is the use of RomanizedBengali for regular interactions. To overcome the existing research'slimitations, in this study, we develop an annotated dataset of 10K Bengaliposts consisting of 5K actual and 5K Romanized Bengali tweets. We implementseveral baseline models for the classification of such hateful posts. Wefurther explore the interlingual transfer mechanism to boost classificationperformance. Finally, we perform an in-depth error analysis by looking into themisclassified posts by the models. While training actual and Romanized datasetsseparately, we observe that XLM-Roberta performs the best. Further, we witnessthat on joint training and few-shot training, MuRIL outperforms other models byinterpreting the semantic expressions better. We make our code and datasetpublic for others.

Hate Speech and Offensive Language Detection in Bengali