Abstract
In order to improve the accuracy of automatic translation of business English, an optimized design of business English translation teaching platform is proposed based on the logistic model combined with deep learning. After using the logistic model to analyze the semantic features of business English translation, the deep learning model is used to segment and mine English images, and the automated lexical feature analysis of business English translation is carried out by using contextual feature matching and adaptive semantic variable finding methods to extract the amount of correlation features between words and vocabulary and to correct the differences in translation in a specific business context to improve the accuracy of English translation. The software design of the platform is carried out under the logistic model, and the platform is mainly divided into a vocabulary database module, an English information processing module, a web interface module, and a human-computer interaction interface module. The test results show that the accuracy of business English translation using this method is good, and the automatic translation capability is strong.
Highlights
As machine English translation technology continues to mature, the use of machine English translation for English translation can greatly reduce the time of manual translation and improve translation efficiency [1]. e study of English translation methods based on machine translation has an important role in promoting English education as well as improving the reading efficiency of foreign language literature
In the process of translating business English, the uncertainty and randomness of business English’s own context lead to poor accuracy of business English machine translation, which requires the optimal design of a business English translation teaching platform, combined with the improved design of algorithms for business English machine translation, to improve the accuracy and efficiency of business English translation, and the research of related teaching platform design methods has received great attention [2]
E machine algorithm for business English translation currently mainly adopts the limit learning machine algorithm, the machine English translation correction algorithm of support vector machine, and the autoregressive analysis method [3], which combines the semantic features of business English translation for the analysis of language environment and automatic translation feature matching in the translation process to improve the accuracy of business English translation, and uses this as the basis for the teaching platform design of business English translation with high teaching quality [4]
Summary
Foreign Languages & International Education College, Quzhou University, Quzhou 324000, Zhejiang, China. In order to improve the accuracy of automatic translation of business English, an optimized design of business English translation teaching platform is proposed based on the logistic model combined with deep learning. After using the logistic model to analyze the semantic features of business English translation, the deep learning model is used to segment and mine English images, and the automated lexical feature analysis of business English translation is carried out by using contextual feature matching and adaptive semantic variable finding methods to extract the amount of correlation features between words and vocabulary and to correct the differences in translation in a specific business context to improve the accuracy of English translation. E software design of the platform is carried out under the logistic model, and the platform is mainly divided into a vocabulary database module, an English information processing module, a web interface module, and a human-computer interaction interface module. After using the logistic model to analyze the semantic features of business English translation, the deep learning model is used to segment and mine English images, and the automated lexical feature analysis of business English translation is carried out by using contextual feature matching and adaptive semantic variable finding methods to extract the amount of correlation features between words and vocabulary and to correct the differences in translation in a specific business context to improve the accuracy of English translation. e software design of the platform is carried out under the logistic model, and the platform is mainly divided into a vocabulary database module, an English information processing module, a web interface module, and a human-computer interaction interface module. e test results show that the accuracy of business English translation using this method is good, and the automatic translation capability is strong
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