Research on Machine Translation of Ancient Chinese Classics by Integrating Different Semantic Knowledge
[Purpose/significance]This article aims to explore whether integrating different semantic knowledge into machine translation models can enhance the effectiveness of machine translation and which type of semantic knowl-edge plays a more significant role.The purpose is to support the research in machine translation and the inheritance and dissemination of Chinese excellent traditional culture.[Method/process]The study selected 300,000 pairs of me-ticulously processed"Ancient Chinese-Modern Chinese"parallel corpora from the"Twenty-Four Histories"as experi-mental data.Based on the neural machine translation model OpenNMT,it integrated word boundary knowledge,part-of-speech knowledge,entity knowledge,and dependency syntax knowledge into the training process of the machine translation model through three different feature fusion methods.[Result/conclusion]The integration of different se-mantic knowledge with the model has varying impacts on the translation effectiveness of classical texts.Word boundary knowledge,part-of-speech knowledge,and entity knowledge contribute to the machine translation task,with entity knowledge making the largest contribution,while the role of dependency syntax knowledge has no obvious effect.
ancient bookssemantic knowledgeTwenty-Four Historiesmachine translation
NETL
NSTL
万方数据
