LXPER Index 2.0: Improving Text Readability Assessment Model for L2 English Students in Korea

TL;DR

This paper presents an improved text readability assessment model tailored for L2 English learners in Korea, utilizing an expanded corpus and achieving higher accuracy in grade-level prediction.

Contribution

It introduces an enhanced readability model specifically designed for L2 English texts in Korea, addressing the low accuracy of existing models.

Findings

Significant accuracy improvement in grade-level assessment

Expanded and labeled CoKEC-text corpus used for training

Model tailored for Korean ELT curriculum texts

Abstract

Developing a text readability assessment model specifically for texts in a foreign English Language Training (ELT) curriculum has never had much attention in the field of Natural Language Processing. Hence, most developed models show extremely low accuracy for L2 English texts, up to the point where not many even serve as a fair comparison. In this paper, we investigate a text readability assessment model for L2 English learners in Korea. In accordance, we improve and expand the Text Corpus of the Korean ELT curriculum (CoKEC-text). Each text is labeled with its target grade level. We train our model with CoKEC-text and significantly improve the accuracy of readability assessment for texts in the Korean ELT curriculum.