GECTurk: Grammatical Error Correction and Detection Dataset for Turkish

TL;DR

This paper introduces GECTurk, a large Turkish grammatical error correction dataset created through a novel synthetic data pipeline, along with baseline models and experiments demonstrating its effectiveness and transferability.

Contribution

It presents a new synthetic data generation pipeline for Turkish GEC, along with a high-quality dataset, baseline models, and extensive experiments on transferability.

Findings

Synthetic data improves GEC performance for Turkish.

Baseline models achieve strong results on in-domain data.

The dataset supports transfer learning and robustness in out-of-domain scenarios.

Abstract

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.