Application of LSTM Neural Network Technology Embedded in English Intelligent Translation

Abstract

With the rapid development of computer technology, the loss of long-distance information in the transmission process is a prominent problem faced by English machine translation. The self-attention mechanism is combined with convolutional neural network (CNN) and long-term and short-term memory network (LSTM). An English intelligent translation model based on LSTM-SA is proposed, and the performance of this model is compared with other deep neural network models. The study adds SA to the LSTM neural network model and constructs the English translation model of LSTM-SA attention embedding. Compared with other deep learning algorithms such as 3RNN and GRU, the LSTM-SA neural network algorithm has faster convergence speed and lower loss value, and the loss value is finally stable at about 8.6. Under the three values of adaptability, the accuracy of LSTM-SA neural network structure is higher than that of LSTM, and when the adaptability is 1, the accuracy of LSTM-SA neural network improved the fastest, with an accuracy of nearly 20%. Compared with other deep learning algorithms, the LSTM-SA neural network algorithm has a better translation level map under the three hidden layers. The proposed LSTM-SA model can better carry out English intelligent translation, enhance the representation of source language context information, and improve the performance and quality of English machine translation model.

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Retracted: Application of LSTM Neural Network Technology Embedded in English Intelligent Translation

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”