Revisiting Classification Taxonomy for Grammatical Errors

ACL ARR 2025 February Submission636 Authors

Abstract: Grammatical error classification plays a crucial role in language learning systems, but existing classification taxonomies often lack rigorous validation, leading to inconsistencies and unreliable feedback. In this paper, we revisit previous classification taxonomies for grammatical errors by introducing a systematic and qualitative evaluation framework. Our approach examines four aspects of a taxonomy, i.e., exclusivity, coverage, balance, and usability. Then, we construct a high-quality grammatical error classification dataset annotated with multiple classification taxonomies and evaluate them grounding on our proposed evaluation framework. Our experiments reveal the drawbacks of existing taxonomies. Our contributions aim to improve the precision and effectiveness of error analysis, providing more understandable and actionable feedback for language learners.

Paper Type: Short

Research Area: Resources and Evaluation

Research Area Keywords: corpus creation, automatic evaluation of datasets, GEC

Contribution Types: Data resources, Data analysis

Languages Studied: English

Submission Number: 636