Construction of Chinese Classical-Modern Translation Model Based on Pre-trained Language Model
This study aims to construct and validate a Chinese ancient-modern translation model based on pre-trained language models,providing strong technical support for the research of ancient Chinese and the inheritance and dissemination of cultural heritage.The study selected a total of 300,000 pairs of meticulously processed parallel corpora from the"Twenty-Four Histories"as the experimental dataset and developed a new translation model—Siku-Trans.This model innovatively combines Siku-RoBERTa(as the encoder)and Siku-GPT(as the decoder),designed specifically for translating ancient Chinese,to build an efficient en-coder-decoder architecture.To comprehensively evaluate the performance of the Siku-Trans model,the study introduced three models as control groups:OpenNMT,SikuGPT,and SikuBERT_UNILM.Through comparative analysis of the performance of each model in ancient Chinese translation tasks,we found that Siku-Trans exhibits significant advantages in terms of translation accuracy and fluency.These results not only highlight the effectiveness of combining Siku-RoBERTa with Siku-GPT as a training strategy but also provide important references and insights for in-depth research and practical applications in the field of ancient Chi-nese translation.
Language modelMachine translationAncient Chinese translationSiku-RoBERTaSiku-GPTSiku-Trans
国家科技期刊平台
NSTL
万方数据
