  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.
