ОРЧУУЛГЫН СУРГАЛТ ДАХЬ ХИЙМЭЛ ОЮУНЫ ТЕХНОЛОГИЙН ХЭРЭГЛЭЭ

Abstract

This study investigates the potential for implementation and its impact on integrating artificial intelligence (AI) into translation training. It compares traditional and AI-based translation training systems, highlighting their advantages and disadvantages, and will be present the results of the study. Owing to significant advancements in AI, translation technologies have achieved unprecedented levels of precision and optimized efficiency. Traditional translation training has been constrained by its time-consuming nature, high costs of space, and operational expenses. Therefore, a transition to new translation methodologies has become imperative. Neural Machine Translation (NMT) Algorithm: Utilizes encode-decode mechanisms to convert the source text into the target language. This process enhances translation accuracy by processing data while retaining semantic meaning. Statistical Machine Translation (SMT) Algorithm: Processes translations based on statistical models, improving accuracy by selecting the most probable translations. The study's findings reveal that AI-based translation systems are 97% more accurate compared to traditional translation methods. Improvements in students' translation skills were evident from the quality of their translated texts, emphasizing the effectiveness of the new technology. Teachers’ satisfaction with AI-based training averaged 92%, further validating the system's effectiveness in academic settings. This marks the beginning of a new era in enhancing the learning process for translation students.