@inproceedings{bi-etal-2025-dongbamie,
    title = "{D}ongba{MIE}: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictograms",
    author = "Bi, Xiaojun  and
      Li, Shuo  and
      Xing, Junyao  and
      Wang, Ziyue  and
      Luo, Fuwen  and
      Qiao, Weizheng  and
      Han, Lu  and
      Sun, Ziwei  and
      Li, Peng  and
      Liu, Yang",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.51/",
    doi = "10.18653/v1/2025.findings-emnlp.51",
    pages = "976--990",
    ISBN = "979-8-89176-335-7",
    abstract = "Dongba pictographic is the only pictographic script still in use in the world. Its pictorial ideographic features carry rich cultural and contextual information. However, due to the lack of relevant datasets, research on semantic understanding of Dongba hieroglyphs has progressed slowly. To this end, we constructed DongbaMIE - the first dataset focusing on multimodal information extraction of Dongba pictographs. The dataset consists of images of Dongba hieroglyphic characters and their corresponding semantic annotations in Chinese. It contains 23,530 sentence-level and 2,539 paragraph-level high-quality text-image pairs. The annotations cover four semantic dimensions: object, action, relation and attribute. Systematic evaluation of mainstream multimodal large language models shows that the models are difficult to perform information extraction of Dongba hieroglyphs efficiently under zero-shot and few-shot learning. Although supervised fine-tuning can improve the performance, accurate extraction of complex semantics is still a great challenge at present."
}