@inproceedings{tiao-etal-2026-deepsemantics,
    title = "{D}eep{S}emantics at {S}em{E}val-2026 Task 9: Label-Wise Optimization with Adaptive Focal Loss for Polarization Manifestation Identification",
    author = "Tiao, Eliasse  and
      Edou, Josue  and
      Gohouede, Mahugnon",
    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-1.210/",
    pages = "1632--1640",
    ISBN = "979-8-89176-414-9",
    abstract = "In this paper, we present our system for SemEval-2026 Task 9, which focuses on the fine-grained identification of polarization manifestations in multilingual social media content.Our approach combines transformer-based encoders (RoBERTa-base for English and Afro-XLM-R-small for Hausa) within aOne-vs-Rest (OvR) framework, complemented by controlled oversampling, Adaptive Focal Loss, and label-wise threshold optimization. To mitigate severe class imbalance and label sparsity, we adopt language-specific optimization strategies supported by pairwise {\ensuremath{\chi}}2 independence analysis.Our system achieves macro-F1 scores of 0.464 in English and 0.192 in Hausa on the official test sets, ranking 5th in Hausa and 14th in English on the official leaderboard."
}