GECTurk: Grammatical Error Correction and Detection Dataset for Turkish
Atakan Kara Farrin Marouf Sofian Andrew Bond Gözde Gül Şahin
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.