Research on Machine Translation for English Majors Based on Transformer Architecture

With the acceleration of globalization, the demand for English professional translation in cross-cultural communication, international trade, academic research, cultural communication and other fields continues to rise, which promotes the rapid development and wide application of machine translation technology. However, although the current machine translation model has improved the translation efficiency, it still has some limitations in dealing with complex language structures and the mismatch between semantics and context. In this regard, based on practical problems, this paper will deeply explore the application feasibility of Transformer architecture in the field of machine translation, and propose a brand-new implementation scheme of neural machine translation model, aiming at strengthening the generalization ability and adaptability of the model and improving the translation quality. Practice has proved that the overall model is based on the Transformer architecture, and incorporates the multi-granularity feature extraction and word frequency information processing mechanism, which enables the model to capture the complex structure of the language more comprehensively, and can deal with the problems of polysemy and rare words, thus improving the translation effect of the model, increasing the BLEU value by 5.3, and providing a higher quality solution for English professional translation.