Intelligent Bilingual Reading Translation System Based on Natural Language Processing

In this paper, we propose a novel hybrid framework to address the challenges of domain adaptability and translation accuracy in current bilingual reading intelligent translation systems. The framework integrates the strong expressive power of Transformer network, the Topic capture advantage of Embedding Topic Model (ETM), and the fine-grained feature learning ability of Masked self-attention mechanism. By applying ETM in the preprocessing stage to refine the topic information of the source language text, and incorporating the Masked self-attention mechanism into the Transformer model to optimize the sequence understanding and translation process, we build a system that can generate more natural and domain adaptable translation output.