Intelligent Proofreading Method for English Translation Based on Deep Neural Network

To enhance the accuracy and efficiency of English translation proofreading, this paper proposes an intelligent proofreading method based on deep neural networks. The method begins by constructing a Recurrent Neural Network (RNN) model, utilizing deep learning techniques to determine sentence breaks and word segmentation in English sentences. Subsequently, a Seq2Seq model is implemented to preprocess the translation sentences. The process further involves decomposing an undirected graph into a first-order chain conditional random field to obtain feature weight vectors for proofreading and designing an English translation word alignment model. Proofreading training is performed using the log-likelihood method. A deep neural network model, trained within the TensorFlow framework, is fine-tuned to replace irrelevant sentence features with new corpus data, achieving high-quality English translation proofreading. Experimental results demonstrate an accuracy of 99.6% and a proofreading time of only 6 min, highlighting the proposed method’s exceptional efficiency and effectiveness in English translation proofreading.