Tag
This paper proposes a diagnosis and optimization framework for cross-domain and cross-lingual generalization in offensive language detection, decomposing performance degradation into dataset and language effects, and quantifying the trade-off between multilingual capability and source-task performance.
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.