A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Loss
This work addresses readability assessment for NLP applications, offering incremental improvements through hybrid methods.
The authors tackled readability assessment by proposing a BERT-based model with feature projection and length-balanced loss, achieving state-of-the-art results on benchmark datasets, including near-perfect 99% accuracy on one English dataset and comparable performance to human experts.
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.