@inproceedings{lin-etal-2026-team,
    title = "Team {YTY} at {S}em{E}val 2026 task 12: Option-Aware Retrieval and Cross-Encoder Reasoning Framework for Abductive Event Reasoning",
    author = "Lin, Junxin  and
      Meng, Zhichao  and
      Jiang, Lianxin",
    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-1.249/",
    pages = "1979--1987",
    ISBN = "979-8-89176-414-9",
    abstract = "We describe a unified system for SemEval-2026 Task 9 on multilingual polarization detection. The task requires binary polarization detection, multi-label target type classification, and multi-label manifestation identification across languages and events with severe class imbalance. Our approach combines (i) targeted data augmentation for low-frequency labels, (ii) merged multitask fine-tuning of Subtask 2 and Subtask 3, and (iii) model fusion to improve cross-lingual stability. Subtask 1 predictions are derived via calibrated inference from the multi-label head. On the development set, multitask training consistently out-performs single-task variants, and fusion yields additional gains, especially for rare labels. We also report ablations and error analyses, highlighting remaining challenges such as implicit polarization and partial-label uncertainty."
}