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

A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Loss