Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study
This addresses the problem of efficient and accurate grammatical error correction for Arabic, an underexplored language in GEC, though it is incremental as it adapts an existing text editing framework to a new domain.
The paper tackled grammatical error correction (GEC) for Arabic, a morphologically rich language, by introducing a text editing approach that derives edit tags from data without language-specific rules, achieving state-of-the-art results on two benchmarks and performing on par on two others, with models over six times faster than existing systems.
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.