An Investigation of English Translation Optimization Strategies Based on the Self-Attention Mechanism of the Transformer Model

Abstract

Aiming at the accuracy problem in English translation, this study proposes a solution based on the Transformer model self-attention mechanism, which aims to improve the accuracy of English translation. The core objective of this paper is to deeply analyze and explore the English translation optimization strategy based on the Transformer model self-attention mechanism. By carefully analyzing the basic principles of the self-attention mechanism and elaborating its unique advantages in the English translation task, we aim to construct a set of effective translation optimization framework. On this basis, we designed and implemented a series of experiments to verify the effectiveness of the proposed strategy. The experimental results show that the adoption of the model can significantly improve the quality and efficiency of English translation, not only making significant progress in translation accuracy, but also optimizing translation speed. This finding not only enriches the theoretical system of English translation technology, but also provides new ideas and methods for translation quality improvement in practical applications.