@inproceedings{wang-etal-2026-mancc,
    title = "{M}an{CC}: A Task-Anchored Benchmark for {M}anchu{--}Classical {C}hinese Cross-Lingual Modeling",
    author = "Wang, Meiqi  and
      Sun, Xiaoxin  and
      Wang, Dongjie  and
      Yu, Ruixin  and
      Heng, Xiantao  and
      Wang, Shuo  and
      Huang, Zhen  and
      Zhao, Peng  and
      Wang, Suhua  and
      Yin, Minghao",
    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.1359/",
    pages = "27271--27292",
    ISBN = "979-8-89176-395-1",
    abstract = "Research in cross-lingual modeling for historical and extremely low-resource languages is hindered by the absence of standardized evaluation benchmarks. To address this, we present ManCC{---}the first task-anchored benchmark for Manchu{--}Classical Chinese translation. ManCC consists of a high-quality parallel corpus of 16,627 sentence pairs, derived from the Qing-dynasty historical text Manwen Laodang-Taizong, and a reproducible evaluation protocol that combines automatic metrics (BLEU and chrF) with a three-dimensional human assessment (fidelity, fluency, linguistic normativity). Through systematic evaluation across three model families (non-pretrained, multilingual pretrained, and large language models), we find that linguistic differences significantly influence performance, broader language coverage in multilingual pretraining facilitates low-resource transfer, and automatic metrics often fail to capture essential errors in historical translation{---}underscoring the necessity of human evaluation. ManCC not only provides foundational resources for Manchu{--}Classical Chinese translation but also establishes a diagnosable, reproducible platform for cross-lingual modeling of historical low-resource languages."
}