2025 ACL ACL 2025

Multilingual Grammatical Error Annotation: Combining Language-Agnostic Framework with Language-Specific Flexibility

Abstract

AbstractGrammatical Error Correction (GEC) relies on accurate error annotation and evaluation, yet existing frameworks, such as errant, face limitations when extended to typologically diverse languages. In this paper, we introduce a standardized, modular framework for multilingual grammatical error annotation. Our approach combines a language-agnostic foundation with structured language-specific extensions, enabling both consistency and flexibility across languages. We reimplement errant using stanza to support broader multilingual coverage, and demonstrate the framework’s adaptability through applications to English, German, Czech, Korean, and Chinese, ranging from general-purpose annotation to more customized linguistic refinements. This work supports scalable and interpretable GEC annotation across languages and promotes more consistent evaluation in multilingual settings. The complete codebase and annotation tools can be accessed at https://github.com/open-writing-evaluation/jp_errant_bea.

🧭 Keyword Pioneer — typoloigical diversity
🐝 Cross-Pollinator — Artificial Intelligence, Deep Learning, Interdisciplinary, Machine Learning, Natural Language Processing, Speech & Audio
🌉 Interdisciplinary Bridge — Artificial Intelligence and Interdisciplinary and Machine Learning and Natural Language Processing