# Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Model

arXiv: https://arxiv.org/abs/1912.05957 · HTML: https://mlsanity.com/paper/1912.05957/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2019.

## Built on by (most cited first, 7 of 7)

- [Trends, Limitations and Open Challenges in Automatic Readability Assessment Research](https://mlsanity.com/paper/2105.00973/) (2021)
- [A Neural Pairwise Ranking Model for Readability Assessment](https://mlsanity.com/paper/2203.07450/) (2022)
- [An Open Multilingual System for Scoring Readability of Wikipedia](https://mlsanity.com/paper/2406.01835/) (2024)
- [Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment](https://mlsanity.com/paper/2107.04268/) (2021)
- [A Transfer Learning Based Model for Text Readability Assessment in German](https://mlsanity.com/paper/2207.06265/) (2022)
- [Eye Tracking Based Cognitive Evaluation of Automatic Readability Assessment Methods](https://mlsanity.com/paper/2502.11150/) (2025)
- [Cross-corpus Readability Compatibility Assessment for English Texts](https://mlsanity.com/paper/2306.09704/) (2023)

## Abstract (arXiv)

Evaluating the readability of a text can significantly facilitate the precise expression of information in written form. The formulation of text readability assessment involves the identification of meaningful properties of the text regardless of its length. Sophisticated features and models are used to evaluate the comprehensibility of texts accurately. Despite this, the problem of assessing texts' readability efficiently remains relatively untouched. The efficiency of state-of-the-art text readability assessment models can be further improved using deep reinforcement learning models. Using a hard attention-based active inference technique, the proposed approach makes efficient use of input text and computational resources. Through the use of semi-supervised signals, the reinforcement learning model uses the minimum amount of text in order to determine text's readability. A comparison of the model on Weebit and Cambridge Exams with state-of-the-art models, such as the BERT text readability model, shows that it is capable of achieving state-of-the-art accuracy with a significantly smaller amount of input text than other models.