mlsanityData to 7 Oct 2026

Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features

arXiv 2109.12258 ↗First posted 25 Sep 2021cs.CL cs.AIcited by 36

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Abstract (arXiv)

We report two essential improvements in readability assessment: 1. three novel features in advanced semantics and 2. the timely evidence that traditional ML models (e.g. Random Forest, using handcrafted features) can combine with transformers (e.g. RoBERTa) to augment model performance. First, we explore suitable transformers and traditional ML models. Then, we extract 255 handcrafted linguistic features using self-developed extraction software. Finally, we assemble those to create several hybrid models, achieving state-of-the-art (SOTA) accuracy on popular datasets in readability assessment. The use of handcrafted features help model performance on smaller datasets. Notably, our RoBERTA-RF-T1 hybrid achieves the near-perfect classification accuracy of 99%, a 20.3% increase from the previous SOTA.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). Builds on 21 of its 108 references that are in the map. First posted on arXiv 25 Sep 2021.

Builds on

21 of 108 references.

References · 21

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
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  12. 2019Text Readability Assessment for Second Language Learnerscited by 54
9 more
  1. 2020Fast and Accurate Neural CRF Constituency Parsingcited by 50
  2. 2019Supervised and Unsupervised Neural Approaches to Text Readabilitycited by 45
  3. 2020Linguistic Features for Readability Assessmentcited by 35
  4. 2021Trends, Limitations and Open Challenges in Automatic Readability Assessment Researchcited by 28
  5. 2021ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysiscited by 16
  6. 2016Readability-based Sentence Ranking for Evaluating Text Simplificationcited by 5
  7. 2020LXPER Index 2.0: Improving Text Readability Assessment Model for L2 English Students in Koreacited by 4
  8. 2020LXPER Index: a curriculum-specific text readability assessment model for EFL students in Koreacited by 2
  9. 2018A Machine Learning Approach to Persian Text Readability Assessment Using a Crowdsourced Datasetcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 13; 2024: 7; 2025: 7; 2026: 3.

36 papers.

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  1. 2023LFTK: Handcrafted Features in Computational Linguisticscited by 19
  2. 2023Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approachcited by 15
  3. 2022A Neural Pairwise Ranking Model for Readability Assessmentcited by 14
  4. 2023ChatLog: Carefully Evaluating the Evolution of ChatGPT Across Timecited by 12
  5. 2023Learning to Paraphrase Sentences to Different Complexity Levelscited by 11
  6. 2025Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectorscited by 9
  7. 2023Prompt-based Learning for Text Readability Assessmentcited by 8
  8. 2025A Large and Balanced Corpus for Fine-grained Arabic Readability Assessmentcited by 8
  9. 2022Automatic Readability Assessment of German Sentences with Transformer Ensemblescited by 7
  10. 2022A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Losscited by 7
  11. 2022Arabic Word-level Readability Visualization for Assisted Text Simplificationcited by 7
  12. 2025Linguistic Blind Spots of Large Language Modelscited by 6
24 more
  1. 2025Opportunities and Challenges of LLMs in Education: An NLP Perspectivecited by 6
  2. 2025EqualizeIR: Mitigating Linguistic Biases in Retrieval Modelscited by 5
  3. 2022Uniform Complexity for Text Generationcited by 4
  4. 2023Traditional Readability Formulas Compared for Englishcited by 4
  5. 2024Language Models Don't Learn the Physical Manifestation of Languagecited by 4
  6. 2024Multi-Objective Linguistic Control of Large Language Modelscited by 4
  7. 2023Ling-CL: Understanding NLP Models through Linguistic Curriculacited by 3
  8. 2023Complexity-Guided Curriculum Learning for Text Graphscited by 3
  9. 2024Guidelines for Fine-grained Sentence-level Arabic Readability Annotationcited by 3
  10. 2026Zero-shot Large Language Models for Automatic Readability Assessmentcited by 3
  11. 2023BasahaCorpus: An Expanded Linguistic Resource for Readability Assessment in Central Philippine Languagescited by 2
  12. 2024Strategies for Arabic Readability Modelingcited by 2
  13. 2022Auto-Select Reading Passages in English Assessment Tests?cited by 1
  14. 2023Efficient Machine Translation Corpus Generationcited by 1
  15. 2024FPT: Feature Prompt Tuning for Few-shot Readability Assessmentcited by 1
  16. 2023Exploring Linguistic Features for Turkish Text Readabilitycited by 0
  17. 2023Cross-corpus Readability Compatibility Assessment for English Textscited by 0
  18. 2023Measuring Adversarial Datasetscited by 0
  19. 2024A study of Vietnamese readability assessing through semantic and statistical featurescited by 0
  20. 2024What Differentiates Educational Literature? A Multimodal Fusion Approach of Transformers and Computational Linguisticscited by 0
  21. 2025Readability Measures and Automatic Text Simplification: In the Search of a Constructcited by 0
  22. 2025Hierarchical Ranking Neural Network for Long Document Readability Assessmentcited by 0
  23. 2026Flesch-Kincaid Readability Depends Only on the Topic Distribution in Long Texts under Topic Modelscited by 0
  24. 2026Analyzing Traditional and Neural Approaches to Multilingual Readability Assessmentcited by 0