Identifying Styles of Cross-Language Classics with Pre-Trained Models

[Objective] This paper uses pre-trained language models to explore and study the linguistic style of canonical texts, aiming to improve their connotation quality. [Methods] We compared the performance of five pre-trained language models with the deep learning model Bi-LSTM-CRF on the cross-lingual canonical ancient Chinese-English corpus. The selected works include The Analects of Confucius, The Tao Te Ching, The Book of Rites, The Shangshu, and The Warring States Curse. We also examined the lexicon-based canonical language style. [Results] The SikuBERT pre-trained language model achieved 91.29% precision, 91.76% recall, and 91.52% in concordance mean F1 for recognizing canonical words. The modern Chinese translation yielded deeper semantic meaning, clearer ideographic referents, and more vivid and flexible word combinations than the original canonical words. [Limitations] This study only chose specific pre-Qin classical texts and their translations. More research is needed to examine the models’performance in other domains. [Conclusions] The pre-trained language model SikuBERT could effectively analyze language style differences of cross-lingual canonical texts, which promotes the dissemination of classic Chinese works. © 2023 Chinese Academy of Sciences. All rights reserved.