Analysis of Feature Extraction Model for Machine Korean Translation Judging System

Abstract

Language translation is becoming increasingly widespread and diversified as internationalization and information technology advance significantly. Nonetheless, there are cultural variations across different nations, and there will unavoidably be some variations brought on by translation software. While the machine translation system has attained a high level of development, the issues with the older machine translation methods still remain and cannot be fully resolved. The efficiency of Korean-Chinese translation must thus be improved by creating a more effective model for converting translation information. This has significant implications for the development of international relationships, commercial interactions, cultural development, and commerce between China and South Korea. The translation is the rendering of words into other languages so that they may be understood by both individuals. Nevertheless, there are several flaws and inconsistencies in the present Korean-to-Chinese translational information processing method. Thus, this study suggests a Diverse Deep Embedding Data Accumulation (DDEDA) algorithm for the translation framework. We utilized the Korean-Chinese corpus dataset for the research. Utilizing translation accuracy, precision, recall, error rate, and translation time, the performance of the suggested approach was examined and compared with previously employed methodologies. The findings demonstrate that the proposed method has taken 43s for translation.