Optimization Strategy for Multilingual Neural Machine Translation System Integrating Transformer Architecture

With the continuous deepening of global integration and the rapid development of information technology, multilingual neural machine translation (NMT) has become an important means of promoting the dissemination of international relations. This article will focus on the research of multilingual translation algorithms based on the Transformer framework, and adjust the number of self attention mechanisms. Based on this, the algorithm will be optimized to improve the accuracy and efficiency of multilingual translation. Meanwhile, this article also introduces a dynamic learning rate adjustment and periodic evaluation mechanism to ensure that the established model has the best performance in multilingual situations. Within 24-72 hours, the BLEU (Bilingual Evaluation Understudy) scores of three translations (English-French, English-German, English-Chinese) showed a stepwise upward trend. The research findings of this article will provide a new theoretical basis and practical approach for the development of NMT, and provide an effective technical support for achieving multilingual information exchange. The research findings of this article will have important practical significance for promoting cross linguistic communication in China's multilingual environment.