An Automatic Assessment and Optimization Algorithm for English Translation Software Combining Deep Learning and Natural Language Processing

As a key medium to eliminate language barriers and enhance international understanding, English translation software plays an important role in the rapid and accurate circulation of information. This article aims to explore the application of deep learning and natural language processing technology in the optimization of English translation software, and proposes an innovative automatic assessment and optimization algorithm. Traditional optimization methods of translation software are limited by manual features and rules, and it is difficult to adapt to complex and changeable translation needs. Therefore, this study combines the powerful feature learning ability of deep learning with the deep understanding of language structure by natural language processing, and designs a novel algorithm. By automatically extracting useful features from the bilingual corpus and learning the complex mapping relationship between the source language and the target language, the algorithm has significantly improved the translation quality. The results show that, compared with traditional methods, the method proposed in this article is excellent in reducing word error rate and improving translation fluency, which provides a new direction for the development of machine translation.