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.- Anthology ID:
- 2020.nlptea-1.3
- Volume:
- Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications
- Month:
- December
- Year:
- 2020
- Address:
- Suzhou, China
- Venue:
- NLP-TEA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 20–24
- Language:
- URL:
- https://aclanthology.org/2020.nlptea-1.3
- DOI:
- Cite (ACL):
- Bruce W. Lee and Jason Lee. 2020. LXPER Index 2.0: Improving Text Readability Assessment Model for L2 English Students in Korea. In Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications, pages 20–24, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- LXPER Index 2.0: Improving Text Readability Assessment Model for L2 English Students in Korea (Lee & Lee, NLP-TEA 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.nlptea-1.3.pdf