@inproceedings{dao-etal-2026-puerai,
    title = "{P}uer{AI} at {S}em{E}val-2026 Task 5: Homograph Appropriateness Assessment via {D}e{BERT}a Contrastive Regression and Contextual Grouping",
    author = "Dao, Jiaxu  and
      Li, Zhuoying  and
      Ma, Hangchao  and
      Tong, Jinli  and
      Lan, Xiaoli  and
      Lu, Yifan  and
      Yang, Zhanji",
    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-1.41/",
    pages = "284--289",
    ISBN = "979-8-89176-414-9",
    abstract = "To assess homograph appropriateness in narrative contexts for SemEval-2026 Task 5, we propose a contrastive regression framework. This approach combines candidate sense definitions with full narrative texts to establish an MSE regression baseline, further enhanced by a contextual grouping ranking loss that models relative rationality among senses. Evaluated on the official AmbiStory dataset, our method consistently outperforms the baseline in accuracy and Spearman correlation. These results validate the efficacy of relative order modeling for capturing fine-grained semantic nuances in complex narratives. The code is available at: https://github.com/daojiaxu/Semeval2026task5."
}