@inproceedings{bao-etal-2026-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2026 Task 9: Hybrid Augmentation and Regularization Strategies for Multilingual Polarization Type Classification",
    author = "Bao, Di  and
      Wang, Jin  and
      Zhang, Xuejie",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.99/",
    pages = "699--705",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper introduces a system based on fine-tuned pretrained language models, which is constructed for SemEval 2026 Task 9: Multilingual Polarization Type Classification. The task aims to perform multi-label polarization classification on texts covering 22 languages, identifying five types of polarization: political, racial/ethnic, religious, gender/sexual, and others. The main challenges of the task lie in handling uneven data distribution across languages, extreme class imbalance, and the complexity of cross-lingual semantic understanding. To address these challenges, a training framework integrating hybrid augmentation and multi-strategy regularization is proposed. Based on XLM-RoBERTa-large, the framework combines feature-space Mixup augmentation, an asymmetric loss function, adversarial training, and exponential moving average. Multi-label decisions are made through dynamic threshold optimization. Experimental results show that the proposed method achieves a macro-F1 score of 0.48 on the validation set, effectively improving classification performance and generalization capability in multilingual and imbalanced scenarios."
}