Application of Deep Learning Algorithm in the Design of Interactive English-Chinese Translation System

With the development of global economy and culture, cross-cultural exchanges are also increasing, and the status of interactive English-Chinese translation system in its application is also increasing. Traditional machine translation is often accompanied by various limitations, such as incomplete language structure and insufficient compact of context. Due to these problems, this study uses deep learning algorithms to study and aims to improve the quality of translation and user experience. Research methods include collecting user feedback and manually evaluation data, as well as analyzing user behavior data. By training and adapting a large number of parallel corpora, we designed an English-Chinese interactive translation system based on deep learning. Experimental results show that the system is significantly better than conventional systems in terms of accuracy, fluency and naturalness. Users show higher interest and satisfaction with deep learning-based systems and stay longer during use. These results demonstrate the potential of deep learning algorithms to improve the quality of translation and user experience. Future studies could further improve the model performance to achieve a faster, more accurate, more natural, and smoother translation and interaction experience.