Strategies for Improving Accuracy and Fluency of Cultural Tourism Translations with the Help of Emerging Technologies

Abstract

Abstract This paper proposes the preliminary architecture of the cultural tourism corpus in accordance with the requirements of cultural tourism translation accuracy and fluency and further optimizes the structure of the architecture by combining it with the cultural tourism translation quality evaluation standards. Using the simple Bayesian classifier and machine learning technology, respectively, the cultural tourism corpus data and text information are collected, inputted, organized, and classified sequentially. Using natural language processing technology, an encoder-decoder framework is constructed to semantically analyze the preprocessed corpus data information, and proposed algorithm performance evaluation criteria are proposed. Simulation experiments are set up to evaluate the model’s translation quality in conjunction with practical applications. The proposed model in this paper is superior to other models in terms of translation fluency, and the TER index of the model in this paper reaches 72.589%. The model proposed in this paper has a translation accuracy of above 90% after six practical application tests. To understand the cultural background, 52.1% of tourists are interested in using the model proposed in this paper.