@inproceedings{wu-etal-2026-ncl-uor,
    title = "{NCL}-{U}o{R} at {S}em{E}val-2026 Task 5: Embedding-Based Methods, Fine-Tuning, and {LLM}s for Word Sense Plausibility Rating",
    author = "Wu, Tong  and
      Markchom, Thanet  and
      Liang, Huizhi(elly)",
    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-1.242/",
    pages = "1930--1937",
    ISBN = "979-8-89176-414-9",
    abstract = "Word sense plausibility rating requires predicting the human-perceived plausibility of a given word sense on a 1{--}5 scale in the context of short narrative stories containing ambiguous homonyms. This paper systematically compares three approaches: (1) embedding-based methods pairing sentence embeddings with standard regressors, (2) transformer fine-tuning with parameter-efficient adaptation, and (3) large language model (LLM) prompting with structured reasoning and explicit decision rules. The best-performing system employs a structured prompting strategy that decomposes evaluation into narrative components (precontext, target sentence, ending) and applies explicit decision rules for rating calibration. The analysis reveals that structured prompting with decision rules outperforms both fine-tuned models and embedding-based approaches, and that prompt design matters more than model scale for this task."
}