@inproceedings{chen-etal-2026-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2026 Task 10: Pretrained {D}istil{BERT} Models for Conspiracy Marker Extraction and Detection",
    author = "Chen, Junpei  and
      Zhang, You  and
      Wang, Jin  and
      Xu, Dan  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-1.105/",
    pages = "741--747",
    ISBN = "979-8-89176-414-9",
    abstract = "In this paper, we present our submission to the SemEval-2026 Psycholinguistic Conspiracy Shared Task (Task 10), which consists of two tasks: conspiracy marker extraction and conspiracy detection. For conspiracy marker extraction, we formulate the problem as a token classification task and fine-tune pretrained language models, achieving performance above the official baseline and ranking 6th on the final leaderboard. For conspiracy detection, we apply data preprocessing, regularization, and ensemble inference strategies,resulting in improvements over the baseline and a 10th-place ranking. Overall, our results demonstrate the effectiveness of pretrained language models for both tasks."
}