Translator attribution of <i>Hongloumeng</i>: using entropy-based features and machining learning algorithm

Abstract This study utilized machine learning algorithms and entropy-based features to identify translators of two English translations of Hongloumeng, a great classical Chinese novel written in the mid-18th century. The translations under examination were completed, respectively, by David Hawkes and the Yangs (Yang Hsien-yi and Gladys Yang). Two feature sets were extracted as input for the identification of translator styles: wordform features (wordform unigrams, bigrams, and trigrams) and part-of-speech (POS) features (POS unigrams, bigrams, and trigrams). Additionally, four machine learning classifiers were tested: linear support vector machines (SVMs), linear discriminant analysis (LDA), random forest (RF), and multilayer perceptron (MLP). Analysis of feature importance and SHAP value identified the most influential features within each classifier. Results showed that LDA achieved the best performance, with 81 per cent accuracy in distinguishing between translations, showing promise for translator identification. In contrast, MLP struggled to reliably differentiate between translations, achieving only 50 per cent accuracy. Furthermore, POS features had the greatest influence in SVM and LDA, while wordform features dominated in RF. SHAP analysis revealed that Hawkes’ translation tended to exhibit higher POS unigram and lower POS trigram entropy compared to the Yangs’. This increased contribution of POS unigrams and trigrams suggests a link to explicitation differences in translation. In summary, the combination of machine learning and entropy-based stylometric features shows potential for automatic translator identification and analysis.