Transformer-based cross-cultural intelligent translation system for international communication

This study proposes a Transformer-based cross-cultural intelligent translation system to enhance international communication.By integrating attention mechanisms and large-scale multilingual datasets encompassing 47,850 samples across seven languages from 35 countries, the model achieves 97.3% accuracy in predicting language competency while ensuring contextual fluency and cultural adaptability.The approach outperforms traditional and BERT-based methods, offering a scalable solution for multilingual, multicultural contexts.Language is a vital bridge for cross-cultural communication, especially in global collaborations.However, traditional translation systems struggle with contextual accuracy and cultural inclusivity.Previous studies have explored neural machine translation enhancements, such as GANs, BiLSTM generators, and syntax-aware methods.While effective, these approaches often face limitations in low-resource languages and cultural adaptability.A hybrid deep learning framework combining Transformer architecture and attention mechanisms was developed.The proposed model achieved 97.3% accuracy, 96.8% precision, 95.6% recall, and 96.4% F1-score.These results outperform state-of-the-art baselines, demonstrating superior performance in cross-cultural translation.