@inproceedings{qiao-etal-2025-disc,
    title = "{DISC}: Plug-and-Play Decoding Intervention with Similarity of Characters for {C}hinese Spelling Check",
    author = "Qiao, Ziheng  and
      Zhou, Houquan  and
      Liu, Yumeng  and
      Li, Zhenghua  and
      Zhang, Min  and
      Zhang, Bo  and
      Li, Chen  and
      Zhang, Ji  and
      Huang, Fei",
    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.1373/",
    pages = "28312--28324",
    ISBN = "979-8-89176-251-0",
    abstract = "One key characteristic of the Chinese spelling check (CSC) task is that incorrect characters are usually similar to the correct ones in either phonetics or glyph. To accommodate this, previous works usually leverage confusion sets, which suffer from two problems, i.e., difficulty in determining which character pairs to include and lack of probabilities to distinguish items in the set. In this paper, we propose a light-weight plug-and-play DISC (i.e., decoding intervention with similarity of characters) module for CSC models. DISC measures phonetic and glyph similarities between characters and incorporates this similarity information only during the inference phase. This method can be easily integrated into various existing CSC models, such as ReaLiSe, SCOPE, and ReLM, without additional training costs. Experiments on three CSC benchmarks demonstrate that our proposed method significantly improves model performance, approaching and even surpassing the current state-of-the-art models."
}