Deep Learning Strategies for Improving Machine Translation Effectiveness
Lantian Wei · 2024
By introducing an improved deep learning model, this study aims to enhance the quality of machine translation, strengthen the robustness of the model, and expand its generalization ability. Advanced sequence-to-sequence models, multi-head attention mechanisms, and adaptive learning strategies are utilized to achieve significant performance enhancements in the task of translating multiple language pairs, especially demonstrating exceptional capabilities in handling complex language structures and various types of texts. These outcomes underscore the vast potential of deep learning in the realm of machine translation and pave the way for new paths in technological innovation.