Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study

2025-03-02

AI Summary

This work addresses the low editing efficiency and heavy reliance on handcrafted rules in Arabic grammatical error correction (GEC), a challenge exacerbated by Arabic’s rich morphology. We propose the first rule-free, data-driven text editing framework for Arabic GEC. Methodologically, we formulate GEC as a sequence labeling task using automatically induced edit labels, integrating multi-representation edit modeling, model ensembling, and fine-tuning of pretrained language models. Key contributions include: (i) the first Arabic-specific edit label taxonomy; and (ii) simultaneous optimization of accuracy, inference speed (over 6× faster than prior SOTA systems), and interpretability. Our approach achieves state-of-the-art (SOTA) performance on two of four major Arabic GEC benchmarks and matches SOTA on the other two. All code, data, and models are publicly released.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Addresses grammatical error correction for morphologically rich languages like Arabic.
• Introduces a data-driven text editing approach without language-specific edits.
• Achieves state-of-the-art results and improves efficiency for Arabic GEC.

Innovation

• Data-driven edit tags for Arabic GEC
• Achieves SOTA on Arabic benchmarks
• Six times faster than existing systems