CoPol at SemEval-2026 Task 9: Modeling Polarization Type Co-occurrence with Label Correlation Networks

AbstractPOLAR-LDA is a label-dependency–aware system for SemEval-2026 Task 9 (multi-label polarization type classification) that augments an mDeBERTa-v3-base encoder with a Label Correlation Network (language-specific directed co-occurrence matrices + GAT), Asymmetric Loss tuned for extreme positive scarcity, and a language-grouped ensemble. The system scores 0.567 macro F1 across 22 languages (range 0.784 Hindi — 0.256 Italian) and shows clear ablation gains (ASL +0.041, LCN +0.030, ensemble +0.018). Key findings: absolute data voids (0–1 positive examples) form an unrecoverable floor for supervised learning; label co-occurrence is culturally situated (e.g., political↔religious in Indic vs. political↔racial in some Western languages) and benefits from language-specific graphs; and per-label training volume predicts cross-lingual performance better than linguistic family. Limitations are honest and important: noisy AL estimates under scarcity, an incoherent residual “other” category, and domain mismatch between pretraining corpora and polarization discourse. Overall, the paper offers a strong shared-task system and useful empirical diagnostics—practical and well-executed, but incrementally novel methodologicall