A Novel Neural Machine Translation Technique for an Effective Korean Translation System

The growing demand for smooth communication between Korean and English speakers emphasizes the need for the development of accurate and efficient translation systems. Existing translation technologies often struggle to cope with the complex linguistic features and contextual nuances of these languages, resulting in poor quality translation outputs. To overcome such challenges, this research presents an enhanced Korean-English translation system using Transformer-based Neural Machine Translation (NMT). Leveraging the Korean Parallel corpora dataset, the model is trained to effectively learn rich contextual representations and syntactic relationships, thereby improving translation accuracy. The self-attention mechanism inherent in the Transformer model enables the handling of long-range dependencies, which enhances the processing of idiomatic expressions and structural dissimilarities. Experimental evaluation demonstrates the model’s strong performance, achieving a BLEU score of 42.8, a METEOR score of 38.5, a TER (Translation Edit Rate) of 24.3%, and a ChrF score of 56.7. These metrics confirm the system’s ability to produce contextually accurate translations, showing significant improvements over baseline NMT models. The experimental results highlight the potential of Transformer-based approaches to enhance the quality of Korean-English machine translation for practical use in intercultural communication, content localization, and language learning software.