Before revision:  In our approach, Bengali texts are first comprehensively preprocessed, before classifying them into political, personal, geopolitical, and religious hates, by employing neural ensemble of different transformer-based neural architectures (i.e., monolingual Bangla BERT-base, multilingual BERT-cased and uncased, and XLM-RoBERTa) , followed by identifying important terms with sensitivity analysis and layer-wise relevance propagation (LRP) to provide human-interpretable explanations.
After revision:  In our approach, Bengali texts are first comprehensively preprocessed, before classifying them into political, personal, geopolitical, and religious hates, by employing neural ensemble of different transformer-based neural architectures (i.e., monolingual Bangla BERT-base, multilingual BERT-cased and uncased, and XLM-RoBERTa) . Subsequently, important (most and least) terms are identified with sensitivity analysis and layer-wise relevance propagation (LRP) to provide human-interpretable explanations.