Currently, the majority of research in grammatical error correction (GEC) is concentrated on universal languages, such as English and Chinese. Many low-resource languages lack accessible evaluation corpora. How to efficiently construct high-quality evaluation corpora for GEC in low-resource languages has become a significant challenge. To fill these gaps, in this paper, we present a framework for constructing GEC corpora. Specifically, we focus on Indonesian as our research language and construct an evaluation corpus for Indonesian GEC using the proposed framework, addressing the limitations of existing evaluation corpora in Indonesian. Furthermore, we investigate the feasibility of utilizing existing large language models (LLMs), such as GPT-3.5-Turbo and GPT-4, to streamline corpus annotation efforts in GEC tasks. The results demonstrate significant potential for enhancing the performance of LLMs in low-resource language settings. Our code and corpus can be obtained from https://github.com/GKLMIP/GEC-Construction-Framework.

本研究解决了低资源语言（如印尼语）缺乏可用评估语料库的问题，并提出了一种构建语法错误纠正（GEC）语料库的框架。通过利用现有的大型语言模型（如GPT-3.5-Turbo和GPT-4），研究表明该框架能够显著提升低资源语言设置中GEC任务的性能。

印尼语语法错误纠正的简单有效语料库构建框架