aiqbench analysis
This paper proposes a layered taxonomy for annotating grammatical errors in Chinese learner writing, aiming to bridge computational error correction with pedagogical analysis. By evaluating the scheme using five large language models, the study assesses its consistency and coverage against automatically extracted edits. The work represents a foundational step toward standardizing data for training and improving specialized AI models in low-resource or specific linguistic contexts.
A Layered Taxonomy for Chinese Learner Grammatical Error Annotation
arXiv:2609.02153v1 Announce Type: new Abstract: Grammatical error annotation in Chinese learner writing requires labels that are both consistent and linguistically meaningful. This paper proposes a layered scheme linking computational Chinese grammatical error correction (CGEC) with pedagogical error analysis. The scheme first identifies character- and punctuation-level orthographic errors, labeling them by edit operation and subtype. Other errors receive a three-layer core label combining edit operation, linguistic domain, and part of speech, with optional Chinese-specific extensions for aspect, modality, comparison, argument structure, and complements. Drawing on CGEC resources, learner-error taxonomies, and Mandarin grammar, the taxonomy is evaluated through a coverage analysis of automatically extracted MuCGEC edits and a preliminary consistency study in which five large language models apply it to a sample. The results support the layered approach while identifying category boundaries requiring further refinement.
Abstract:Grammatical error annotation in Chinese learner writing requires labels that are both consistent and linguistically meaningful. This paper proposes a layered scheme linking computational Chinese grammatical error correction (CGEC) with pedagogical error analysis. The scheme first identifies character- and punctuation-level orthographic errors, labeling them by edit operation and subtype. Other errors receive a three-layer core label combining edit operation, linguistic domain, and part of speech, with optional Chinese-specific extensions for aspect, modality, comparison, argument structure, and complements. Drawing on CGEC resources, learner-error taxonomies, and Mandarin grammar, the taxonomy is evaluated through a coverage analysis of automatically extracted MuCGEC edits and a preliminary consistency study in which five large language models apply it to a sample. The results support the layered approach while identifying category boundaries requiring further refinement.
Computation and Language (cs.CL)
arXiv:2609.02153 [cs.CL]
(or arXiv:2609.02153v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.02153 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 223 words
Related coverage
Chinese Grammatical Error Correction: A Survey
arXiv · Jun 9, 2026
Toward a Culturally Adapted Chinese Language Agent: A Wizard-of-Oz Study of Nonverbal Behavior in Chinese-German Intercultural Interaction
arXiv · Oct 1, 2026
ArabiGEE: A Hierarchical Taxonomy for Arabic Grammatical Error Explanation
arXiv · Jun 10, 2026
Accurate but Natural? Diagnosing Grammatical and Idiomatic Gaps in Japanese EFL Writing
arXiv · Aug 25, 2026