直接从数据学习阿拉伯语语法纠错的编辑标签，效果更好且速度超快。
Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study
- 基于数据自动学习编辑标签，无需人工设计语言规则。
- 在两个阿拉伯语纠错数据集上达到当前最好效果，另两个持平最优。
- 模型速度比现有系统快六倍以上，适合实际应用。
文本编辑将语法纠错（GEC）视为序列标注问题，为输入词元分配编辑标签，再通过执行这些编辑得到修正后的文本。该方法因高效与可解释性强而受到关注。然而，尽管在英语中被广泛研究，对阿语这类形态丰富的语言，文本编辑仍研究不足。本文提出一种直接从数据中学习编辑标签的方法，无需依赖语言特定的编辑规则。我们在阿语——一种存在方言差异且形态复杂的语言——上验证其有效性，并探讨不同编辑表示方式对模型性能的影响。该方法在两个阿拉伯语GEC基准上达到最先进水平，在另外两个基准上表现与最先进模型相当。此外，模型速度超过现有阿语纠错系统六倍以上，更具实际应用价值。我们还探索了集成模型，证明组合不同模型可进一步提升性能。代码、数据及预训练模型均已公开。
原文摘要 · Abstract (English)
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.
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