English Semantic Coherence Analysis and Translation Optimization Based on Tree-like Long and Short-term Memory Networks

Abstract

 In the context of global integration, cultural exchanges among countries and nations are getting closer and closer, and English, as one of the most widely used languages, the degree of accuracy and coherence of translation have become a crucial issue. With the progress of network technology, Tree-LSTM shows excellent application prospects in the field of language translation and text processing. Based on this, this paper analyzes the existing problems of English translation and analyzes the application of Tree-LSTM in English translation. This paper firstly elaborates the research background, points out the existing problems of English translation, and focuses on the application of Tree-LSTM in text translation for writing. The article clarifies the concept, working principle and advantages of Tree-LSTM application in translation. Subsequently, the factors affecting the translation quality of Tree-LSTM model are analyzed and discussed in light of the importance of English semantic coherence. Then this paper elaborates the workflow steps of Tree-LSTM in the translation process. In terms of enhancing translation quality, this paper further proposes the strategy of utilizing Tree-LSTM to enhance the coherence of translated texts, and introduces relevant advanced technologies such as natural language processing to further enhance the translation quality. Finally, the article summarizes the application scenarios and potentials of Tree-LSTM in the field of translation, further points out the important role of Tree-LSTM in improving the level and quality of translation, and looks forward to its future optimization direction and application areas. Meanwhile, the article points out the problems of traditional translation methods and emphasizes the potential of Tree-LSTM in solving these problems. This article provides certain improvement ideas for the coherence and optimization of English translation, which is of great significance in promoting the development of the field of natural language and text processing. By introducing the assistance of Tree-LSTM model, the accuracy and fluency of English translation can be effectively improved to provide users with better translation services.