We present a set of novel neural supervised and unsupervised approaches for
determining the readability of documents. In the unsupervised setting, we
leverage neural language models, whereas in the supervised setting, three
different neural classification architectures are tested. We show that the
proposed neural unsupervised approach is robust, transferable across languages
and allows adaptation to a specific readability task and data set. By
systematic comparison of several neural architectures on a number of benchmark
and new labelled readability datasets in two languages, this study also offers
a comprehensive analysis of different neural approaches to readability
classification. We expose their strengths and weaknesses, compare their
performance to current state-of-the-art classification approaches to
readability, which in most cases still rely on extensive feature engineering,
and propose possibilities for improvements.

该研究提出了一系列新颖的神经监督和无监督方法来确定文档的易读性。通过在多个基准和新标记的易读性数据集上系统比较几种神经体系结构，提供了不同神经方法进行易读性分类的综合分析，并展示了它们的优缺点。