A Cross-Modal Semantic Alignment Method and Computer-Aided Translation System for English Translation

To address the surge in cross-modal demands for English translation in a globalized context, existing cross-modal semantic alignment algorithms suffer from low accuracy and poor adaptability, and computer-aided translation (CAT) systems struggle to effectively integrate image, audio and text multimodal information. This paper proposes a translation-oriented modality adaptive attention semantic alignment (TMAASA) algorithm and constructs a CAT system based on this algorithm. Multiple original constraints and computational models are designed, and the algorithm is encapsulated and integrated into the system. Simulation experiments verify its performance. Experiments show that the TMAASA algorithm achieves an alignment accuracy of 92.78%, an average alignment error of 0.036, a BLEU-4 value of 58.96, and a single-sentence response time of 0.32s, all outperforming mainstream comparison algorithms. The constructed system is functionally complete and stable, effectively improving the quality and efficiency of English translation. This research provides a new technical path for the application of crossmodal technology in English translation, possessing significant engineering application value and theoretical reference significance.