A Simple Yet Effective Corpus Construction Framework for Indonesian Grammatical Error Correction
This addresses the challenge of building high-quality GEC evaluation resources for low-resource languages like Indonesian, which is incremental as it applies existing methods to a new domain.
The paper tackles the lack of accessible evaluation corpora for grammatical error correction (GEC) in low-resource languages by presenting a framework to construct such corpora, specifically for Indonesian, and demonstrates the potential of using large language models like GPT-3.5-Turbo and GPT-4 to streamline annotation efforts.
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.