  Current Grammar Error Correction (GEC) initiatives tend to focus on major
languages, with less attention given to low-resource languages like Esperanto.
In this article, we begin to bridge this gap by first conducting a
comprehensive frequency analysis using the Eo-GP dataset, created explicitly
for this purpose. We then introduce the Eo-GEC dataset, derived from authentic
user cases and annotated with fine-grained linguistic details for error
identification. Leveraging GPT-3.5 and GPT-4, our experiments show that GPT-4
outperforms GPT-3.5 in both automated and human evaluations, highlighting its
efficacy in addressing Esperanto's grammatical peculiarities and illustrating
the potential of advanced language models to enhance GEC strategies for less
commonly studied languages.
