  For readability assessment, traditional methods mainly employ machine
learning classifiers with hundreds of linguistic features. Although the deep
learning model has become the prominent approach for almost all NLP tasks, it
is less explored for readability assessment. In this paper, we propose a
BERT-based model with feature projection and length-balanced loss (BERT-FP-LBL)
for readability assessment. Specially, we present a new difficulty knowledge
guided semi-supervised method to extract topic features to complement the
traditional linguistic features. From the linguistic features, we employ
projection filtering to extract orthogonal features to supplement BERT
representations. Furthermore, we design a new length-balanced loss to handle
the greatly varying length distribution of data. Our model achieves
state-of-the-art performances on two English benchmark datasets and one dataset
of Chinese textbooks, and also achieves the near-perfect accuracy of 99\% on
one English dataset. Moreover, our proposed model obtains comparable results
with human experts in consistency test.
