The Application of Artificial Intelligence in the Dissemination of Local Culture in Hainan Province: Taking the Dissemination of Bai Yuchan's Thoughts as an Example. (7th October 2022) - Yu, Yuxiu; Wang, Honghai
Abstract : Bai Yuchan's Taoist thought is an important part of Taoist health-preserving thought. Excavating, sorting out, and translating Bai Yuchan's Taoist thought will not only help increase cultural self-confidence and protect traditional culture but also become an important medium for foreign exchanges. With the advent of the digital age, artificial intelligence has helped the dissemination of excellent traditional Chinese culture with its unique technological advantages, improving the effectiveness, intensity, and breadth of cultural dissemination. In domain machine translation, whether domain terms can be correctly translated plays a decisive role in the translation quality. It is of practical significance to effectively integrate domain terms into neural machine translation models and improve the translation quality of domain terms. This paper proposes a method of incorporating new Bai Yuchan's thought term information as prior knowledge into neural machine translation. Using the term dictionary constructed from Bai Yuchan's thought bilingual terminology knowledge base as a medium, two different knowledge integration methods are proposed and compared: (1) term replacement, which means using the target term to replace the source term on the source language side, and (2) term addition, which means splicing the source term and the target term on the source language side and both the source language side and the target language side. Use identifiers as special external knowledge to identify the beginning and end of the target term. Based on the Chinese-English bilingual alignment corpus of New Bai Yuchan's thoughts and the constructed Chinese-English alignment termbase, the experiments are carried out. The results show that on the test set, the BLEU value of the proposed method is 6.38 and 6.38 higher than the baseline experiments, respectively, which proves that the proposed method can effectively incorporate domain terminology knowledge into the translation model and improve the translation quality of domain terminology.