  Grammatical Error Correction has seen significant progress with the recent
advancements in deep learning. As those methods require huge amounts of data,
synthetic datasets are being built to fill this gap. Unfortunately, synthetic
datasets are not organic enough in some cases and even require clean data to
start with. Furthermore, most of the work that has been done is focused mostly
on English. In this work, we introduce a new organic data-driven approach,
clean insertions, to build parallel Turkish Grammatical Error Correction
datasets from any organic data, and to clean the data used for training Large
Language Models. We achieve state-of-the-art results on two Turkish Grammatical
Error Correction test sets out of the three publicly available ones. We also
show the effectiveness of our method on the training losses of training
language models.
