@inproceedings{hua-etal-2025-bird,
    title = "{BIRD}: Bronze Inscription Restoration and Dating",
    author = "Hua, Wenjie  and
      Nguyen, Hoang H  and
      Ge, Gangyan",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1073/",
    pages = "21189--21201",
    ISBN = "979-8-89176-332-6",
    abstract = "Bronze inscriptions from early China are fragmentary and difficult to date. We introduce BIRD (Bronze Inscription Restoration and Dating), a fully encoded dataset grounded in standard scholarly transcriptions and chronological labels. We further propose an allograph-aware masked language modeling framework that integrates domain- and task-adaptive pretraining with a Glyph Net (GN), which links graphemes and allographs. Experiments show that GN improves restoration, while glyph-biased sampling yields gains in dating."
}