@inproceedings{wu-etal-2026-cyut-semeval,
    title = "{CYUT} at {S}em{E}val-2026 Task 9: Monolingual vs. Multilingual {L}o{RA} Tuning for Multicultural and Multievent Polarization Detection",
    author = "Wu, Shih-Hung  and
      Liao, Yun-Kuang  and
      Su, Shih-Siang  and
      Jian, Yi-Min",
    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-1.209/",
    pages = "1621--1631",
    ISBN = "979-8-89176-414-9",
    abstract = "This study addresses SemEval-2026 Task 9 on Detecting Multilingual, Multicultural, and Multievent Online Polarization, exploring the performance differences between monolingual and multilingual LoRA (Low-Rank Adaptation) fine-tuning techniques when processing online polarization phenomena. The research points out that online polarization is not only a language phenomenon, but a complex social language problem highly influenced by cultural contexts and event backgrounds. To address the limitation of existing research that only treats polarization as a binary classification, this study participates in three levels of subtasks: Subtask 1: Polarization Detection, Subtask 2: Polarization Type Classification (e.g., politics, religion), and Subtask 3: Manifestation Identification (analyzing rhetorical strategies that construct polarization, such as stereotypes and dehumanization narratives). This study aims to establish a more contextually grounded and diagnostic model analysis framework to enhance the model{'}s generalization ability and fairness in cross-lingual environments. By exploring different fine-tuning configurations to build a robust ensemble system, the experimental results show that our approach demonstrates exceptional proficiency in the Chinese domain, securing the 1st place ranking in Subtask 1 (Polarization Detection) for Chinese. Furthermore, we observe that while the monolingual LoRA strategy exhibits strong performance in specific languages like Chinese, integrating it with multilingual LoRA models via ensembling provides the diverse features crucial for identifying complex cross-cultural rhetoric."
}