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

arXiv: https://arxiv.org/abs/2010.13374 · HTML: https://mlsanity.com/paper/2010.13374/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features](https://mlsanity.com/paper/2109.12258/) (2021)
- [LFTK: Handcrafted Features in Computational Linguistics](https://mlsanity.com/paper/2305.15878/) (2023)
- [Traditional Readability Formulas Compared for English](https://mlsanity.com/paper/2301.02975/) (2023)
- [Auto-Select Reading Passages in English Assessment Tests?](https://mlsanity.com/paper/2205.06961/) (2022)

## Abstract (arXiv)

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.