Machine Automatic Translation System Based on Artificial Intelligence Processor

In the era of deepening globalization, the demand for machine automated translation systems is growing, as they facilitate cross-linguistic communication. However, these systems often struggle with accuracy, contextual fluency, and personalization, limiting their effectiveness. This study addresses these shortcomings by leveraging the bidirectional training capabilities of artificial intelligence processors and BERT models. Our approach effectively captures nuances in the source language, enabling the generation of natural and fluent target language text. Additionally, the parallel computing capabilities of these processors significantly accelerate translation speed, meeting real-time translation demands. We also train personalized translation models through user behavior analysis, catering to diverse user preferences in translation style. Our experimental results demonstrate a substantial improvement in the BLEU score compared to traditional phrase-based statistical machine translation, rule-based machine translation, and early neural machine translation methods. Specifically, the system achieves a BLEU score that is significantly higher, indicating improved accuracy and fluency. In terms of speed, the system can process 1000 words in as fast as 0.45 seconds, showcasing its efficiency. The personalized translation effects are also significant, further enhancing the system's adaptability to individual user needs. These findings validate the effectiveness of artificial intelligence processors in enhancing machine automated translation systems and highlight their potential for diverse translation applications.