Organic Data-Driven Approach for Turkish Grammatical Error Correction and LLMs
This addresses the lack of organic data for Turkish Grammatical Error Correction, which is incremental as it adapts existing methods to a new language.
The authors tackled the problem of building parallel datasets for Turkish Grammatical Error Correction without relying on synthetic data, achieving state-of-the-art results on two out of three publicly available test sets and improving training losses for language models.
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.