Study on an Intelligent English Translation Method Using an Improved Convolutional Neural Network Model

Abstract

This study uses an enhanced convolutional neural network (CNN) model for English translation. Traditional translation methods often struggle with complex language structures, prompting the adoption of deep learning techniques, particularly CNNs, in natural language processing. The research outlines CNN fundamentals and their relevance in language processing, elucidating the design and implementation of an improved CNN model. To address CNN limitations in maintaining coherence during the translation of lengthy texts, historical attention mechanisms are incorporated to enhance translation performance. Experimental validation conducted in MATLAB demonstrates notable improvements in translation task performance, evidenced by significant increases in BLEU scores. Results highlight the model's capacity to integrate contextual information, thereby enhancing translation coherence and accuracy. Additionally, the study establishes a mathematical framework for augmenting CNNs with attention mechanisms, providing valuable insights for the development of intelligent English translation systems.