  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.
