Beyond Polarity in Bengali Texts: Can Multi-Task Modeling Capture Sarcasm Intensity and Classification?

Sarcasm detection in Bengali is challenging due to implicit sentiment, contextual dependence, and limited linguistic resources. This study proposes a novel multi-task learning framework that jointly predicts sarcasm polarity and estimates sarcasm intensity using a Cross-Task Attention mechanism to enable effective feature sharing and capture fine-grained sarcasm patterns. We preprocess a balanced Bengali dataset of 25,636 comments with a language-specific normalization pipeline and generate intensity scores using a transformer-based scoring model. The framework is evaluated on several pre-trained transformers, including BanglaBERT, Bangla-ELECTRA, mBERT, XLM-RoBERTa, and DistilBERT. BanglaBERT achieves the best performance with 77.38% accuracy and an F1-score of 0.7761, while ELECTRA and BERT-Base Uncased perform competitively. For interpretability, we apply LIME to Bengali predictions and SHAP to English-translated outputs. Overall, the proposed multi-task learning framework uses a shared transformer encoder with a Cross-Task Attention mechanism to capture sarcasm patterns more effectively.