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<p>The English translation of Chinese classics is crucial for cultural dissemination and East-West exchanges, but assessing their readability poses challenges due to linguistic and cultural differences. This study applies natural language processing and machine learning to evaluate the readability of five English translations of <i>The Analects</i> by D. C. Lau, James Legge, William Jennings, Edward Slingerland, and Burton Watson. Using a corpus with 4236 lines for model training and 2824 held-out lines for application and reporting, 114 readability features were analyzed. Two machine learning techniques, XGBoost and BP Neural Network, were employed to develop customized readability models. The results, evaluated using mean squared error (MSE) and <i>R</i>², with Pearson correlations reported for feature screening and descriptive analysis, reveal that both models achieve high predictive performance, with the BP Neural Network slightly outperforming XGBoost. Key factors influencing readability include sentence length, syntactic structure, lexical complexity, and lexical density (used as a proxy for information density). This study offers a novel method for assessing and improving the readability of multiple English translations of The Analects. While the approach may be potentially transferable to other Chinese classics, such generalization requires further validation.</p>

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Readability assessment of English translations of Chinese classics: a study based on XGBoost and BP neural networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月15日

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  • Liwei Yang,
  • Guijun Zhou

摘要

The English translation of Chinese classics is crucial for cultural dissemination and East-West exchanges, but assessing their readability poses challenges due to linguistic and cultural differences. This study applies natural language processing and machine learning to evaluate the readability of five English translations of The Analects by D. C. Lau, James Legge, William Jennings, Edward Slingerland, and Burton Watson. Using a corpus with 4236 lines for model training and 2824 held-out lines for application and reporting, 114 readability features were analyzed. Two machine learning techniques, XGBoost and BP Neural Network, were employed to develop customized readability models. The results, evaluated using mean squared error (MSE) and R², with Pearson correlations reported for feature screening and descriptive analysis, reveal that both models achieve high predictive performance, with the BP Neural Network slightly outperforming XGBoost. Key factors influencing readability include sentence length, syntactic structure, lexical complexity, and lexical density (used as a proxy for information density). This study offers a novel method for assessing and improving the readability of multiple English translations of The Analects. While the approach may be potentially transferable to other Chinese classics, such generalization requires further validation.