Analysis of Semantic Deviation in AI Translation and Linguistic Optimization Paths - Feng, Chuhan (International Scientific Technical and Economic Research, 2025)
With the rapid development of artificial intelligence technology, AI translation plays an increasingly important role in cross-language communication. However, AI translation still faces the problem of semantic bias, which affects the accuracy and fluency of translation. This paper analyzes the main manifestations and causes of semantic bias in AI translation, explores the limitations of the model itself, the impact of training data problems and language structure differences on translation, and proposes corresponding language optimization paths, including improving context understanding ability, optimizing training data and models, enhancing semantic reasoning ability, and strengthening cultural adaptation and localization. Studies have shown that through multi-dimensional optimization, semantic bias can be effectively reduced and translation quality can be improved. Finally, the future development direction of AI translation technology is prospected, especially the further optimization path in a multilingual and multicultural environment, which provides new ideas and directions for future AI translation research.