Joshualee2 at SemEval-2026 Task 9: Cross-Lingual Transformer-Based Polarization Detection

AbstractThis paper describes our system for POLAR Subtask 1 on multilingual polarization detection. The task involves binary sequence classification over 22 languages, where the model aims to predict whether a given text exhibits polarized discourse. To deal with the multilingual and resource-imbalanced nature of the dataset, we fine-tune the XLM-R, a pre-trained multilingual transformer encoder, using a language-aware sampling strategy that combines all available training data into a unified multilingual corpus. Our system achieves an overall macro-F1 of 0.781 and an average accuracy of 0.823 on the official test set. Results show strong performance in low-resource languages, though some discrepancies indicate remaining class imbalance.