Joint Translation Method for English–Chinese Place Names Based on Prompt Learning and Knowledge Graph Enhancement

In producing English-Chinese bilingual maps, it is usually necessary to translate English place names into Chinese. However, pipeline-based methods for translating place names splits the place name translation task into multiple sub-tasks, carries the risk of error propagation, resulting in lower efficiency and poorer accuracy. Meanwhile, there is relatively little research on place name joint translation. In this regard, the study proposes an English-Chinese place name joint translation method based on prompt learning and knowledge graph enhancement. This method aims to improve the accuracy of English-Chinese place name translation. The proposed method is divided into two parts: The first part is the construction of prompt word template for place name translation. For the translation task of place names, the study first analyzes the characteristics of the transliteration of specific names and the semantic translation of generic names, constructing prompt word templates for the joint translation of ordinary place names. Then, based on the prompt words for ordinary place name translation, it takes into account the translation characteristics of the derived parts in derived place names, constructing a prompt word template for the joint translation of derived place names. Ultimately, leveraging the powerful contextual learning ability of LLM (Large Language Models), it achieves the joint translation of English and Chinese place names. The second part is the construction of the ontology of place name translation knowledge graph. To retrieve relevant knowledge about the input place names, the study designs an ontology for a knowledge graph of place names translation aimed at the English-Chinese place name translation task, combining the needs of English-Chinese place name translation and the semantic relationships between place names. This enhances the contextual information of the input place names and improves the performance of large language models in the English-Chinese place name translation task. Experiments have shown that compared to traditional pipeline-based place name translation methods, the place name translation method proposed in the study has improved performance by 21.26% in ordinary place name translation and an average of 27.70% in the field of derived place name translation. In bilingual map production, the method effectively improves the efficiency and accuracy of toponymic translation. Simultaneously providing reference for place name translation tasks in other languages.