Error detection and correction for English learners using neural models

Abstract

In recent years, the importance of English language learning in Uruguay has been steadily growing. However, the country faces a significant challenge: a shortage of teachers, which makes it difficult for students to achieve an adequate English level. This problem particularly affects children at the initial levels, limiting their progress in language learning. In this context, it is crucial to assess student progress in order to define educational policies. This project builds on the work of previous initiatives that seek to support teachers in correcting exercises written by students. The central purpose of the project is to develop a tool that facilitates the automatic correction of texts written by English learners at the initial stages. The tool must receive the texts and return a corrected version, highlighting the identified errors. Initially, we explored the exclusive use of Large Language Models (LLMs) to address this problem. However,after several experiments, the results were not satisfactory. Given this limitation, we opted for an alternative solution that combines the use of LLMs, a module for detecting differences, and a classifier trained to predict error types, achieving the same correction objective, developing a system composed of three independent modules. We obtained an error corrector based on the Mistral LLM with an F0.5 score of 0.81, and an error classifier with an F1 score of 0.76. Overall, the system achieved an F0.5 performance of 0.68.