Grammatical error correction of Slovenian school essays using large language models

Abstract

Grammatical error correction (GEC) is the task of automatically detecting and correcting grammatical errors in text. Large language models have enabled the development of accurate automated methods for detecting and correcting certain types of errors. In the educational domain, the aim of GEC is to aid teachers in correcting student errors. Excessive paraphrasing is a property of Generative Pre-trained Transformer-based models and is undesirable in the language education context. To avoid this, we develop multiple Slovenian models for correcting errors in spelling, word case (capitalization), word form, and word order. We describe the training data construction, training process, and model evaluation approach using the Šolar-Eval 1.0 corpus of school essays authored by primary and secondary school students. Our quantitative evaluation shows that the developed models have reasonably high accuracy levels, and our qualitative evaluation highlights the strengths and weaknesses of the models and the evaluation process. The analysis reveals multiple challenges and promising future directions for improving both model development and the evaluation process.