Cascading versus Joint Modeling for Hierarchical Offensive Language Detection
Fine-grained offensive language detection organizes labels into a hierarchical structure, for which two modeling paradigms exist: cascaded decomposition and joint multi-task modeling. Prior work rarely provides a direct, controlled comparison of the two paradigms in terms of accuracy, parameter count, and inference latency, and rarely verifies whether a chosen class-imbalance handling strategy is actually optimal. This paper proposes a three-level cascaded detection system whose training strategy is customized per subtask, together with two verification mechanisms. First, a controlled ablation study determines the best class-imbalance handling strategy for each subtask. Second, a joint mu...
| Authors | Ruixing Ren, Junhui Zhao, Xiaoke Sun |
|---|---|
| Primary category | cs.CL |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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