For the Chinese track of SemEval-2026 Task 9 (Detecting Online Polarization), we address two key challenges: polarized content frequently uses implicit language (e.g., homophones and coded terms) to evade moderation, and class distributions exhibit severe long-tail imbalance. We propose a metric learning approach that frames polarization detection as semantic similarity matching, which captures implicit language patterns better than linear decision boundaries. We fine-tune an ERNIE-3.0 encoder with SoftTriple loss and apply ik/iNN retrieval for binary detection (Subtask 1). For multi-label categorization (Subtasks 2 and 3), we transfer learned representations from the detection model and fine-tune with Asymmetric Loss. A priority-based stratified cross-validation strategy ensures minority classes appear across all training folds despite extreme label skew. Evaluated on the official 1,927-sample test set using an end-to-end pipeline, our system achieved Macro-F1 scores of 0.9190 (Rank 6) on Polarization Detection, 0.8244 (Rank 5) on Type Classification, and 0.6670 (Rank 4) on Manifestation Identification.

This paper describes our team’s submission to AbjadMed at AbjadNLP 2026. The task involves classifying Arabic medical question-answer pairs into 82 categories, characterized by a long-tail distribution and significant semantic overlap. While domain-specific Arabic models exist, they are primarily optimized for Named Entity Recognition or span-extraction tasks rather than high-cardinality sequence classification. Consequently, our system adopts a robust optimization approach using a general-purpose encoder. We utilize ARBERTv2 as the backbone, employing Label-Distribution-Aware Margin (LDAM) loss to mitigate class imbalance and Fast Gradient Method (FGM) adversarial training to enhance generalization boundaries. Our approach achieves a Macro-F1 score of 0.4028 on the private test set, demonstrating that advanced optimization techniques can yield competitive performance on specialized taxonomies without requiring domain-specific pre-training.