Grammatical Error Detection and Correction (GEC) tools have proven useful for native speakers and second language learners. Developing such tools requires a large amount of parallel, annotated data, which is unavailable for most languages. Synthetic data generation is a common practice to overcome the scarcity of such data. However, it is not straightforward for morphologically rich languages like Turkish due to complex writing rules that require phonological, morphological, and syntactic information. In this work, we present a flexible and extensible synthetic data generation pipeline for Turkish covering more than 20 expert-curated grammar and spelling rules (a.k.a., writing rules) implemented through complex transformation functions. Using this pipeline, we derive 130,000 high-quality parallel sentences from professionally edited articles. Additionally, we create a more realistic test set by manually annotating a set of movie reviews. We implement three baselines formulating the task as i) neural machine translation, ii) sequence tagging, and iii) prefix tuning with a pretrained decoder-only model, achieving strong results. Furthermore, we perform exhaustive experiments on out-of-domain datasets to gain insights on the transferability and robustness of the proposed approaches. Our results suggest that our corpus, GECTurk, is high-quality and allows knowledge transfer for the out-of-domain setting. To encourage further research on Turkish GEC, we release our datasets, baseline models, and the synthetic data generation pipeline at https://github.com/GGLAB-KU/gecturk.

翻译：语法错误检测与纠正（GEC）工具已被证明对母语者和第二语言学习者具有实用价值。开发此类工具需要大规模平行标注数据，而大多数语言缺乏此类资源。合成数据生成是解决数据稀缺问题的常见做法，但对于土耳其语这类形态丰富的语言而言，由于需要融合音系学、形态学和句法信息的复杂书写规则，该过程并非易事。本研究针对土耳其语提出了一种灵活且可扩展的合成数据生成流水线，通过复现由专家编写的20余条语法与拼写规则（即书写规则），实现复杂的变换函数。利用该流水线，我们从专业编辑的文章中获取了13万句高质量平行语料。此外，我们通过人工标注一组电影评论构建了更具真实性的测试集。我们实现了三个基线模型，将任务分别建模为：i）神经机器翻译、ii）序列标注、iii）基于预训练解码器模型的前缀微调，均取得显著效果。进一步地，我们在跨领域数据集上开展全面实验，以探究所提方法的可迁移性和鲁棒性。结果表明，我们的语料库GECTurk质量优异，能够支持跨领域场景下的知识迁移。为促进土耳其语GEC领域的进一步研究，我们在https://github.com/GGLAB-KU/gecturk 公开了数据集、基线模型及合成数据生成流水线。