@inproceedings{gu-etal-2025-improving,
    title = "Improving Automatic Grammatical Error Annotation for {C}hinese Through Linguistically-Informed Error Typology",
    author = "Gu, Yang  and
      Huang, Zihao  and
      Zeng, Min  and
      Qiu, Mengyang  and
      Park, Jungyeul",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-main.189/",
    pages = "2781--2798",
    abstract = "Comprehensive error annotation is essential for developing effective Grammatical Error Correction (GEC) systems and delivering meaningful feedback to learners. This paper introduces improvements to automatic grammatical error annotation for Chinese. Our refined framework addresses language-specific challenges that cause common spelling errors in Chinese, including pronunciation similarity, visual shape similarity, specialized participles, and word ordering. In a case study, we demonstrated our system{'}s ability to provide detailed feedback on 12-16{\%} of all errors by identifying them under our new error typology, specific enough to uncover subtle differences in error patterns between L1 and L2 writings. In addition to improving automated feedback for writers, this work also highlights the value of incorporating language-specific features in NLP systems."
}