@inproceedings{bao-etal-2025-revisiting,
    title = "Revisiting Classical {C}hinese Event Extraction with Ancient Literature Information",
    author = "Bao, Xiaoyi  and
      Wang, Zhongqing  and
      Gu, Jinghang  and
      Huang, Chu-Ren",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.414/",
    pages = "8440--8451",
    ISBN = "979-8-89176-251-0",
    abstract = "The research on classical Chinese event extraction trends to directly graft the complex modeling from English or modern Chinese works, neglecting the utilization of the unique characteristic of this language. We argue that, compared with grafting the sophisticated methods from other languages, focusing on classical Chinese{'}s inimitable source of {\_}{\_}Ancient Literature{\_}{\_} could provide us with extra and comprehensive semantics in event extraction. Motivated by this, we propose a Literary Vision-Language Model (VLM) for classical Chinese event extraction, integrating with literature annotations, historical background and character glyph to capture the inner- and outer-context information from the sequence. Extensive experiments build a new state-of-the-art performance in the GuwenEE, CHED datasets, which underscores the effectiveness of our proposed VLM, and more importantly, these unique features can be obtained precisely at nearly zero cost."
}