  Deep learning models for automatic readability assessment generally discard
linguistic features traditionally used in machine learning models for the task.
We propose to incorporate linguistic features into neural network models by
learning syntactic dense embeddings based on linguistic features. To cope with
the relationships between the features, we form a correlation graph among
features and use it to learn their embeddings so that similar features will be
represented by similar embeddings. Experiments with six data sets of two
proficiency levels demonstrate that our proposed methodology can complement
BERT-only model to achieve significantly better performances for automatic
readability assessment.

Computation and Language