Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study
Alhafni, Bashar Habash, Nizar
Alhafni, Bashar
Habash, Nizar
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Department
Natural Language Processing
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Type
Conference proceeding
Date
2025
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Language
English
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Abstract
Text editing frames grammatical error correction (GEC) as a sequence tagging problem, where edit tags are assigned to input tokens, and applying these edits results in the corrected text. This approach has gained attention for its efficiency and interpretability. However, while extensively explored for English, text editing remains largely underexplored for morphologically rich languages like Arabic. In this paper, we introduce a text editing approach that derives edit tags directly from data, eliminating the need for language-specific edits. We demonstrate its effectiveness on Arabic, a diglossic and morphologically rich language, and investigate the impact of different edit representations on model performance. Our approach achieves SOTA results on two Arabic GEC benchmarks and performs on par with SOTA on two others. Additionally, our models are over six times faster than existing Arabic GEC systems, making our approach more practical for real-world applications. Finally, we explore ensemble models, demonstrating how combining different models leads to further performance improvements. We make our code, data, and pretrained models publicly available.(1)
Citation
B. Alhafni and N. Habash, “Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study,” Proceedings of the Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 17892–17914, 2025, doi: 10.18653/V1/2025.ACL-LONG.875.
Source
PROCEEDINGS OF THE 63RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1: LONG PAPERS
Conference
63rd Association for Computational Linguistics Meeting-ACL-Annual
Keywords
Grammatical Error Correction, Text Editing, Arabic GEC, Sequence Tagging, Edit Representations, Morphologically Rich Languages, State-of-the-Art Results, Efficient GEC Models
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Source
63rd Association for Computational Linguistics Meeting-ACL-Annual
Publisher
Association for Computational Linguistics