Improving Automatic Grammatical Error Annotation for Chinese Through Linguistically-Informed Error Typology
Yang Gu, Zihao Huang, Min Zeng, Mengyang Qiu, Jungyeul Park
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.
BibTeX
@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://aclanthology.org/2025.coling-main.189/",
pages = "2781--2798"
}