# Linguistic Features for Readability Assessment

arXiv: https://arxiv.org/abs/2006.00377 · HTML: https://mlsanity.com/paper/2006.00377/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2020.

## Built on by (most cited first, 30 of 35)

- [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)
- [Predicting Lexical Complexity in English Texts: The Complex 2.0 Dataset](https://mlsanity.com/paper/2102.08773/) (2021)
- [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)
- [A Baseline Readability Model for Cebuano](https://mlsanity.com/paper/2203.17225/) (2022)
- [AI, write an essay for me: A large-scale comparison of human-written versus ChatGPT-generated essays](https://mlsanity.com/paper/2304.14276/) (2023)
- [Measuring and Modifying the Readability of English Texts with GPT-4](https://mlsanity.com/paper/2410.14028/) (2024)
- [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)
- [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)
- [An Open Multilingual System for Scoring Readability of Wikipedia](https://mlsanity.com/paper/2406.01835/) (2024)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [Uniform Complexity for Text Generation](https://mlsanity.com/paper/2204.05185/) (2022)
- [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)
- [Zero-shot Large Language Models for Automatic Readability Assessment](https://mlsanity.com/paper/2604.24470/) (2026)
- [Strategies for Arabic Readability Modeling](https://mlsanity.com/paper/2407.03032/) (2024)
- [A Simple Post-Processing Technique for Improving Readability Assessment of Texts using Word Mover's Distance](https://mlsanity.com/paper/2103.07277/) (2021)
- [Semi-Supervised Joint Estimation of Word and Document Readability](https://mlsanity.com/paper/2104.13103/) (2021)
- [FPT: Feature Prompt Tuning for Few-shot Readability Assessment](https://mlsanity.com/paper/2404.02772/) (2024)
- [Exploring Linguistic Features for Turkish Text Readability](https://mlsanity.com/paper/2306.03774/) (2023)
- [Cross-corpus Readability Compatibility Assessment for English Texts](https://mlsanity.com/paper/2306.09704/) (2023)
- [A study of Vietnamese readability assessing through semantic and statistical features](https://mlsanity.com/paper/2411.04756/) (2024)
- [Controlling Language Difficulty in Dialogues with Linguistic Features](https://mlsanity.com/paper/2509.14545/) (2025)

## Abstract (arXiv)

Readability assessment aims to automatically classify text by the level appropriate for learning readers. Traditional approaches to this task utilize a variety of linguistically motivated features paired with simple machine learning models. More recent methods have improved performance by discarding these features and utilizing deep learning models. However, it is unknown whether augmenting deep learning models with linguistically motivated features would improve performance further. This paper combines these two approaches with the goal of improving overall model performance and addressing this question. Evaluating on two large readability corpora, we find that, given sufficient training data, augmenting deep learning models with linguistically motivated features does not improve state-of-the-art performance. Our results provide preliminary evidence for the hypothesis that the state-of-the-art deep learning models represent linguistic features of the text related to readability. Future research on the nature of representations formed in these models can shed light on the learned features and their relations to linguistically motivated ones hypothesized in traditional approaches.