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Cross-corpus Readability Compatibility Assessment for English Texts

arXiv 2306.09704 ↗First posted 16 Jun 2023cs.CLcited by 0

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Abstract (arXiv)

Text readability assessment has gained significant attention from researchers in various domains. However, the lack of exploration into corpus compatibility poses a challenge as different research groups utilize different corpora. In this study, we propose a novel evaluation framework, Cross-corpus text Readability Compatibility Assessment (CRCA), to address this issue. The framework encompasses three key components: (1) Corpus: CEFR, CLEC, CLOTH, NES, OSP, and RACE. Linguistic features, GloVe word vector representations, and their fusion features were extracted. (2) Classification models: Machine learning methods (XGBoost, SVM) and deep learning methods (BiLSTM, Attention-BiLSTM) were employed. (3) Compatibility metrics: RJSD, RRNSS, and NDCG metrics. Our findings revealed: (1) Validated corpus compatibility, with OSP standing out as significantly different from other datasets. (2) An adaptation effect among corpora, feature representations, and classification methods. (3) Consistent outcomes across the three metrics, validating the robustness of the compatibility assessment framework. The outcomes of this study offer valuable insights into corpus selection, feature representation, and classification methods, and it can also serve as a beginning effort for cross-corpus transfer learning.

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References · 16

  1. 2017RACE: Large-scale ReAding Comprehension Dataset From Examinationscited by 851
  2. 2022Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehensioncited by 79
  3. 2019Text Readability Assessment for Second Language Learnerscited by 54
  4. 2019Supervised and Unsupervised Neural Approaches to Text Readabilitycited by 45
  5. 2021Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Featurescited by 36
  6. 2020Linguistic Features for Readability Assessmentcited by 35
  7. 2021Trends, Limitations and Open Challenges in Automatic Readability Assessment Researchcited by 28
  8. 2017Large-scale Cloze Test Dataset Created by Teacherscited by 27
  9. 2020Bridging Information-Seeking Human Gaze and Machine Reading Comprehensioncited by 20
  10. 2021BERT Embeddings for Automatic Readability Assessmentcited by 19
  11. 2020Benchmarking Robustness of Machine Reading Comprehension Modelscited by 18
  12. 2021ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysiscited by 16
4 more
  1. 2022A Neural Pairwise Ranking Model for Readability Assessmentcited by 14
  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. 2020Pre-training Text Representations as Meta Learningcited by 5

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