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.

翻译：评估文本的可读性能够显著促进书面信息表达的精确性。文本可读性评估的构建需要识别文本的有意义属性，且不受文本长度限制。当前通常采用复杂特征与模型来准确评估文本的可理解性。尽管如此，如何高效评估文本可读性的问题仍相对未被充分探索。利用深度强化学习模型，可进一步提升现有顶尖文本可读性评估模型的效率。本研究提出一种基于硬注意力主动推理技术的方法，能够高效利用输入文本与计算资源。通过半监督信号，强化学习模型利用最少量的文本即可判断文本的可读性。在Weebit与Cambridge Exams数据集上，与BERT文本可读性模型等最先进模型的对比表明，该模型能以显著少于其他模型的输入文本量达到同等水平的先进准确率。