# Text Readability Assessment for Second Language Learners

arXiv: https://arxiv.org/abs/1906.07580 · HTML: https://mlsanity.com/paper/1906.07580/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2019.

## Built on by (most cited first, 30 of 54)

- [Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features](https://mlsanity.com/paper/2109.12258/) (2021)
- [Trends, Limitations and Open Challenges in Automatic Readability Assessment Research](https://mlsanity.com/paper/2105.00973/) (2021)
- [CEFR-Based Sentence Difficulty Annotation and Assessment](https://mlsanity.com/paper/2210.11766/) (2022)
- [BERT Embeddings for Automatic Readability Assessment](https://mlsanity.com/paper/2106.07935/) (2021)
- [ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment](https://mlsanity.com/paper/2305.14463/) (2023)
- [LFTK: Handcrafted Features in Computational Linguistics](https://mlsanity.com/paper/2305.15878/) (2023)
- [Flesch or Fumble? Evaluating Readability Standard Alignment of Instruction-Tuned Language Models](https://mlsanity.com/paper/2309.05454/) (2023)
- [Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach](https://mlsanity.com/paper/2310.06680/) (2023)
- [From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation](https://mlsanity.com/paper/2406.03030/) (2024)
- [A Neural Pairwise Ranking Model for Readability Assessment](https://mlsanity.com/paper/2203.07450/) (2022)
- [Learning to Paraphrase Sentences to Different Complexity Levels](https://mlsanity.com/paper/2308.02226/) (2023)
- [Automatic Readability Assessment for Closely Related Languages](https://mlsanity.com/paper/2305.13478/) (2023)
- [Annotation Curricula to Implicitly Train Non-Expert Annotators](https://mlsanity.com/paper/2106.02382/) (2021)
- [Prompt-based Learning for Text Readability Assessment](https://mlsanity.com/paper/2302.13139/) (2023)
- [A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment](https://mlsanity.com/paper/2502.13520/) (2025)
- [A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Loss](https://mlsanity.com/paper/2210.10305/) (2022)
- [Arabic Word-level Readability Visualization for Assisted Text Simplification](https://mlsanity.com/paper/2210.10672/) (2022)
- [Standardize: Aligning Language Models with Expert-Defined Standards for Content Generation](https://mlsanity.com/paper/2402.12593/) (2024)
- [An Open Multilingual System for Scoring Readability of Wikipedia](https://mlsanity.com/paper/2406.01835/) (2024)
- [Alignment Drift in CEFR-prompted LLMs for Interactive Spanish Tutoring](https://mlsanity.com/paper/2505.08351/) (2025)
- [Linguistic Blind Spots of Large Language Models](https://mlsanity.com/paper/2503.19260/) (2025)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [Predicting Text Readability from Scrolling Interactions](https://mlsanity.com/paper/2105.06354/) (2021)
- [Traditional Readability Formulas Compared for English](https://mlsanity.com/paper/2301.02975/) (2023)
- [Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment](https://mlsanity.com/paper/2107.04268/) (2021)
- [Under the Microscope: Interpreting Readability Assessment Models for Filipino](https://mlsanity.com/paper/2110.00157/) (2021)
- [Guidelines for Fine-grained Sentence-level Arabic Readability Annotation](https://mlsanity.com/paper/2410.08674/) (2024)
- [Applying Text Mining to Analyze Human Question Asking in Creativity Research](https://mlsanity.com/paper/2501.02090/) (2025)
- [Zero-shot Large Language Models for Automatic Readability Assessment](https://mlsanity.com/paper/2604.24470/) (2026)
- [EXPATS: A Toolkit for Explainable Automated Text Scoring](https://mlsanity.com/paper/2104.03364/) (2021)

## Abstract (arXiv)

This paper addresses the task of readability assessment for the texts aimed at second language (L2) learners. One of the major challenges in this task is the lack of significantly sized level-annotated data. For the present work, we collected a dataset of CEFR-graded texts tailored for learners of English as an L2 and investigated text readability assessment for both native and L2 learners. We applied a generalization method to adapt models trained on larger native corpora to estimate text readability for learners, and explored domain adaptation and self-learning techniques to make use of the native data to improve system performance on the limited L2 data. In our experiments, the best performing model for readability on learner texts achieves an accuracy of 0.797 and PCC of 0.938.