  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.
