Cross-corpus Readability Compatibility Assessment for English Texts

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 corpora. In this study, we propose novel evaluation framework, Cross-corpus text Readability Compatibility Assessment (CRCA) a , to address issue. The framework encompasses three key components: (1) Corpus: CEFR, CLEC, CLOTH, NES, OSP, andRACE. Linguistic features, GloVeword vector representations, and their fusion features were extracted. (2) Classification models: Machine learning methods (XGBoost, SVM) deep (BiLSTM, Attention-BiLSTM) employed. (3) metrics: RJSD, RRNSS, NDCG metrics. Our findings revealed: Validated compatibility, with OSP standing out significantly other datasets. An adaptation effect among corpora, feature classification methods. Consistent outcomes across metrics, validating robustness framework. study offer valuable insights selection, representation, methods, it can also serve beginning effort for cross-corpus transfer learning.