Hybrid NMT model and comparison with existing machine translation approaches

Neural machine translation has transformed automated translation, surpassing traditional methods with its significant accuracy improvements. However, despite its successes, NMT still encounters several challenges, such as handling low-resource languages, maintaining contextual coherence, and addressing ambiguities in translation. This research presents a novel hybrid NMT model to overcome these limitations. It combines the strengths of traditional translation methods with modern deep learning approaches. We conduct a comprehensive comparative analysis of our hybrid model against existing machine translation approaches, including rule-based machine translation (RBMT), SMT, and state-of-the-art NMT systems. Evaluation metrics BLEU is utilized to assess the performance across English-Hindi,English-Marathi,English-Bengali language pairs and domains. Our results demonstrate that the hybrid NMT model achieves superior accuracy and fluency in translation tasks, particularly for low-resource languages and complex sentence structures. This research highlights the potential of combining different machine translation approaches and findings suggest that integrating these methods can significantly improve translation quality. The findings offer valuable insights for future research and development of more robust and versatile translation systems. Our results demonstrate that the hybrid model offers significant improvements in translation accuracy, making it a promising approach for multilingual machine translation tasks. NMT surpasses both RBMT and SMT with a BLEU score of 35.6, highlighting its effectiveness in managing context and semantics. Qualitative assessments suggest that the hybrid model effectively minimizes common translation errors, making it a robust solution for multilingual machine translation tasks. Hybrid Neural Machine Translation (NMT) models are increasingly being applied in real-world applications where the combination of rule-based, statistical, and neural approaches offers distinct advantages.