A Simple Yet Effective Corpus Construction Framework for Indonesian Grammatical Error Correction

TL;DR

This paper introduces a framework for constructing high-quality grammatical error correction (GEC) evaluation corpora for Indonesian, a low-resource language, and explores using large language models to streamline annotation efforts, demonstrating promising results.

Contribution

It presents a novel framework for Indonesian GEC corpus construction and evaluates the use of LLMs to improve annotation efficiency in low-resource settings.

Findings

Effective corpus construction framework for Indonesian GEC

Utilization of GPT-3.5-Turbo and GPT-4 enhances annotation efficiency

Potential for improving GEC performance in low-resource languages

Abstract

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.