Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment

• URL: http://arxiv.org/abs/2107.04268v1
• Date: Fri, 9 Jul 2021 07:26:17 GMT
• Title: Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment
• Authors: Xinying Qiu, Yuan Chen, Hanwu Chen, Jian-Yun Nie, Yuming Shen, Dawei Lu
• Abstract summary: We propose to incorporate linguistic features into neural network models by learning syntactic dense embeddings based on linguistic features. Our proposed methodology can complement BERT-only model to achieve significantly better performances for automatic readability assessment.
• Score: 17.882688516249058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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