mlsanityData to 7 Oct 2026

Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment

arXiv 2107.04268 ↗First posted 9 Jul 2021cs.CLcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 15 of its 59 references that are in the map. First posted on arXiv 9 Jul 2021.

Builds on

15 of 59 references.

References · 15

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  3. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  4. 2019Revealing the Dark Secrets of BERTcited by 300
  5. 2015A Review of Relational Machine Learning for Knowledge Graphscited by 264
  6. 2017A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applicationscited by 260
  7. 2019Assessing BERT's Syntactic Abilitiescited by 191
  8. 2019Text Readability Assessment for Second Language Learnerscited by 54
  9. 2019Supervised and Unsupervised Neural Approaches to Text Readabilitycited by 45
  10. 2020Linguistic Features for Readability Assessmentcited by 35
  11. 2018A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplificationcited by 24
  12. 2017Neural Cross-Lingual Entity Linkingcited by 15
3 more
  1. 2019Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Modelcited by 7
  2. 2016Grammatical Templates: Improving Text Difficulty Evaluation for Language Learnerscited by 3
  3. 2016Neural Networks Models for Entity Discovery and Linkingcited by 1

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Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 1.

3 papers.

  1. 2022A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Losscited by 7
  2. 2022Tapping the Potential of Coherence and Syntactic Features in Neural Models for Automatic Essay Scoringcited by 1
  3. 2024FPT: Feature Prompt Tuning for Few-shot Readability Assessmentcited by 1