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

Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment