mlsanityData to 7 Oct 2026

A Neural Pairwise Ranking Model for Readability Assessment

arXiv 2203.07450 ↗First posted 14 Mar 2022cs.CL cs.AI cs.LGcited by 14

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Abstract (arXiv)

Automatic Readability Assessment (ARA), the task of assigning a reading level to a text, is traditionally treated as a classification problem in NLP research. In this paper, we propose the first neural, pairwise ranking approach to ARA and compare it with existing classification, regression, and (non-neural) ranking methods. We establish the performance of our model by conducting experiments with three English, one French and one Spanish datasets. We demonstrate that our approach performs well in monolingual single/cross corpus testing scenarios and achieves a zero-shot cross-lingual ranking accuracy of over 80% for both French and Spanish when trained on English data. Additionally, we also release a new parallel bilingual readability dataset in English and French. To our knowledge, this paper proposes the first neural pairwise ranking model for ARA, and shows the first results of cross-lingual, zero-shot evaluation of ARA with neural models.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 16 of its 66 references that are in the map. First posted on arXiv 14 Mar 2022.

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16 of 66 references.

References · 16

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  3. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  4. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  5. 2019Text Readability Assessment for Second Language Learnerscited by 54
  6. 2019Supervised and Unsupervised Neural Approaches to Text Readabilitycited by 45
  7. 2020Learning-to-Rank with BERT in TF-Rankingcited by 36
  8. 2021Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Featurescited by 36
  9. 2020Linguistic Features for Readability Assessmentcited by 35
  10. 2018TF-Ranking: Scalable TensorFlow Library for Learning-to-Rankcited by 32
  11. 2019Controlling Text Complexity in Neural Machine Translationcited by 32
  12. 2018Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networkscited by 22
4 more
  1. 2021ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysiscited by 16
  2. 2017Language Modeling by Clustering with Word Embeddings for Text Readability Assessmentcited by 7
  3. 2019Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Modelcited by 7
  4. 2018RankDCG: Rank-Ordering Evaluation Measurecited by 4

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Citing papers per year: 2022: 1; 2023: 6; 2024: 5; 2025: 2.

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  1. 2023Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplificationcited by 14
  2. 2024ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinementcited by 12
  3. 2023Learning to Paraphrase Sentences to Different Complexity Levelscited by 11
  4. 2023Automatic Readability Assessment for Closely Related Languagescited by 9
  5. 2023Prompt-based Learning for Text Readability Assessmentcited by 8
  6. 2022A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Losscited by 7
  7. 2024An Open Multilingual System for Scoring Readability of Wikipediacited by 7
  8. 2024ImpScore: A Learnable Metric For Quantifying The Implicitness Level of Sentencecited by 3
  9. 2023BasahaCorpus: An Expanded Linguistic Resource for Readability Assessment in Central Philippine Languagescited by 2
  10. 2024Strategies for Arabic Readability Modelingcited by 2
  11. 2024FPT: Feature Prompt Tuning for Few-shot Readability Assessmentcited by 1
  12. 2023Cross-corpus Readability Compatibility Assessment for English Textscited by 0
2 more
  1. 2025Text Simplification with Sentence Embeddingscited by 0
  2. 2025Hierarchical Ranking Neural Network for Long Document Readability Assessmentcited by 0