@inproceedings{wang-chen-2026-ccnu,
    title = "{CCNU} at {S}em{E}val-2026 Task 10: Conspiracy Marker Extraction and Detection via Multi-task Learning and {LLM}-based Data Augmentation",
    author = "Wang, Zijun  and
      Chen, Guanyi",
    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-1.58/",
    pages = "402--408",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents the system of CCNU forSemEval-2026 Task 10: Psycholinguistic Con-spiracy Marker Extraction and Detection. Thetask requires identifying fine-grained conspir-acy markers that characterize conspiracy think-ing, as well as determining whether a Redditcomment constitutes conspiratorial discourse.For Conspiracy Marker Extraction (Subtask 1),we adopt a Unified Multi-Task Sequence La-beling Framework that jointly models multi-ple conspiracy markers within a single labelingspace. This formulation enables collaborativelearning across marker types while maintaininga compact architecture. For Conspiracy Detec-tion (Subtask 2), we formulate the problem assentence-level classification. Across both sub-tasks, we apply data augmentation powered bylarge language models and ensemble inferenceto improve robustness and generalization. Oursystem achieves strong performance on Sub-task 1, ranking 3rd on the official test set, anddelivers competitive results on Subtask 2."
}