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This paper compares cascaded and joint multi-task modeling for hierarchical offensive language detection, finding that cascaded architectures achieve higher accuracy at the cost of increased parameters and inference latency, and that class-imbalance handling strategies should be verified via ablation.
Proposes a Hierarchy-Aware RoBERTa framework for classifying cybersecurity vulnerabilities in the CWE taxonomy, demonstrating that hierarchy-aware representation learning is more effective than oversampling techniques for handling class imbalance.