Synthetic Data Augmentation for Bangla Hate Speech Detection Using Slot-Based Semantic Extraction

Novel Framework for Bangla Hate Speech: Developed a robust multi-class classification framework (Hate, Non-Hate, Sarcastic) tailored for implicit hate and sarcasm in low-resource Bangla.

Slot-Based Semantic Extraction: Introduced a structured slot extraction method (TARGET, Negative Expression, Neutral Context, Sarcastic Cue) to capture contextual semantic roles.

Balanced Synthetic Data Augmentation: Built a template-driven, slot-replacement synthetic augmentation technique that expanded a 5,000-comment corpus into a fully balanced 16,302-comment dataset.

State-of-the-Art Performance: Achieved 91.90% accuracy and 91.76% Macro F1-score using BanglaBERT, along with a lightweight TF-IDF+LR baseline reaching 85.00% accuracy, outperforming existing benchmarks.