  Automatic readability assessment is one of the most important applications of
Natural Language Processing (NLP) in education. Since automatic readability
assessment allows the fast selection of appropriate reading material for
readers at all levels of proficiency, it can be particularly useful for the
English education of English as Foreign Language (EFL) students around the
world. Most readability assessment models are developed for the native readers
of English and have low accuracy for texts in the non-native English Language
Training (ELT) curriculum. We introduce LXPER Index, which is a readability
assessment model for non-native EFL readers in the ELT curriculum of Korea. Our
experiments show that our new model, trained with CoKEC-text (Text Corpus of
the Korean ELT Curriculum), significantly improves the accuracy of automatic
readability assessment for texts in the Korean ELT curriculum.
