Research on Semantic Enhancement and Error Detection Algorithm Based on Korean Translation

Traditional translation methods, such as rulebased and statistical machine translation, have problems such as insufficient semantic understanding and blunt translation results. With the development of deep learning technology, Neuromachine Translation (NMT) has achieved remarkable results in Korean translation, but it still needs to optimize semantic understanding and error detection. This paper designs a system architecture that integrates data preprocessing, semantic enhancement module, error detection module and output post-processing. Through deep learning technology, semantic role labeling, semantic dependency analysis and semantic similarity calculation are realized to enhance the semantic understanding ability in the translation process. The error detection module combines the methods based on rules, statistical models and deep learning to comprehensively detect and correct translation errors. The experimental results show that the semantic enhancement algorithm significantly improves the accuracy and fluency of translation, and the error detection algorithm shows high accuracy in detecting translation errors. The algorithm proposed in this paper provides strong support for the translation between Korean and Chinese, and is of great significance to improve the quality of Korean translation.