@inproceedings{akram-fatima-2026-nust,
    title = "{NUST} {P}sy{AI} at {S}em{E}val-2026 Task 10: Parameter-Efficient {R}o{BERT}a for Conspiracy Detection and Character-Level Marker Extraction",
    author = "Akram, Mian Muhammad Husnain  and
      Fatima, Mehwish",
    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-1.170/",
    pages = "1298--1307",
    ISBN = "979-8-89176-414-9",
    abstract = "We present the NUST PsyAI system for SemEval-2026 Task 10 (PsyCoMark), targeting document-level conspiracy detection and character-level psycholinguistic marker extraction from Reddit discourse. Our system ranks 7th in Extraction and 8th in Detection on the leaderboard. We benchmark feature-based and transformer approaches, adopting RoBERTalarge with LoRA for parameter-efficient finetuning. For detection, RB-DET-LoRA outperforms all baselines, achieving weighted F1 0.79 (dev) and 0.76 (test), with robust generalization under blinded evaluation. For extraction, we contrast a unified multi-type BIO scheme with a decomposed per-type setup; the latter mitigates cross-label interference and improves boundary consistency, reaching Overlap F1 of 0.16 (dev) and 0.21 (test). Results reveal a clear asymmetry: detection benefits from contextual semantic modeling, while extraction is limited by sparse supervision and boundary-sensitive evaluation."
}