@inproceedings{zhou-etal-2026-acse,
    title = "{ACSE}: An Ancient Character Semantic-Aware Embedding for Large Language Models",
    author = "Zhou, Zhihan  and
      Shi, Daqian  and
      Shi, Lida  and
      Song, Rui  and
      Qiu, Peiqiang  and
      Diao, Xiaolei  and
      Xu, Hao",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.437/",
    pages = "9000--9012",
    ISBN = "979-8-89176-395-1",
    abstract = "Research on ancient Chinese language is of great significance for tracing Chinese history and civilization. In the field of large language models, studies on the pre-Qin excavated documents such as Oracle Bone Inscriptions, Bronze Inscriptions, and Bamboo Book of Chu remain insufficient. This is because these ancient characters have a low level of digitization, training corpora are extremely scarce, and they typically contain complex and rich semantic information. Therefore, we propose an ancient character semantic-aware embedding for large language models. This embedding integrates both the glyph and lexicality of ancient characters and maps them to the modern Chinese semantic space. We also design a two-stage method for lightweight and parameter-efficient training of the embedding. Finally, we conduct extensive experiments on excavated documents from the pre-Qin period, and the results demonstrate the effectiveness of our approach."
}