LLM-based Code-Switched Text Generation for Grammatical Error Correction

TL;DR

This paper investigates applying large language models to generate code-switched text for grammatical error correction, creating synthetic datasets, and improving correction accuracy for multilingual ESL learners.

Contribution

It introduces a novel synthetic data generation method for code-switched GEC and demonstrates its effectiveness in enhancing correction performance.

Findings

Synthetic CSW GEC data improves model accuracy

Models trained on synthetic data outperform existing systems

First substantial dataset for CSW GEC in ESL contexts

Abstract

With the rise of globalisation, code-switching (CSW) has become a ubiquitous part of multilingual conversation, posing new challenges for natural language processing (NLP), especially in Grammatical Error Correction (GEC). This work explores the complexities of applying GEC systems to CSW texts. Our objectives include evaluating the performance of state-of-the-art GEC systems on an authentic CSW dataset from English as a Second Language (ESL) learners, exploring synthetic data generation as a solution to data scarcity, and developing a model capable of correcting grammatical errors in monolingual and CSW texts. We generated synthetic CSW GEC data, resulting in one of the first substantial datasets for this task, and showed that a model trained on this data is capable of significant improvements over existing systems. This work targets ESL learners, aiming to provide educational technologies that aid in the development of their English grammatical correctness without constraining their natural multilingualism.