ENHANCING TRANSLATION WITH VISUAL AND AUDITORY MODALITIES

This study investigates the impact of integrating visual and auditory modalities into neural machine translation (NMT) processes. Traditional text-based NMT models face limitations in translation quality due to their inability to capture contextual and cultural nuances effectively. This research demonstrates that incorporating visual and auditory elements—such as scene context, character expressions, intonation, and emphasis—leads to significant improvements in translation quality. The study highlights the capacity of multimodal models to preserve cultural and emotional contexts beyond linguistic fidelity. It explores the potential of these models in various applications, including subtitle translation, video game localization, and educational materials. The findings show that visual and auditory modalities enhance the interaction with linguistic context, producing context-aware and culturally aligned content in translation processes. Additionally, this work systematically compares deep learning models such as Transformer, BERT, and GPT, evaluating their characteristics in improving translation quality. The results indicate that new technologies integrating visual and auditory contexts offer significant advantages over traditional text-based models. This has important implications for both theoretical discussions and practical applications.