An informative teaching model of English-Chinese translation in universities based on the perceptual machine model

Abstract

Abstract With the development of globalization, there is an increasing demand for English-Chinese translation talents in the country and society. In this paper, a text classifier based on multilayer perceptron is proposed, an MLP structure containing two layers of feedforward neural network is designed for the characteristics of English-Chinese translation corpus, the training samples are selected with text height mainly below 60 pixels, and the best sliding window of 13×13 is selected by comparison, supplemented by the smoothing method of mathematical morphology. The constructed MLP-based information-based teaching model is then applied to teaching English-Chinese translation in colleges and universities and is mainly evaluated in terms of student evaluation and teaching effectiveness. There were 28.48 percentage points more English-Chinese translation students who thought the class was more interesting than the original, while 20.56% and 20.50% more students evaluated the class as more helpful and less helpful, respectively. The average score of English-Chinese translation students increased by 15.88% compared to the original one through the comparison test between traditional and informational teaching modes. The MLP-based informatization teaching can mobilize students’ enthusiasm to participate in English-Chinese translation learning, strengthen the guidance of translation methods, and improve students’ translation levels.