Research on Strategies for Improving University English Translation Ability for the Digital Era

Abstract

With the increase of international exchange and cooperation and the acceleration of globalization, English translation ability has become one of the key directions for the cultivation of professional talents in colleges and universities. This paper takes the intelligent proofreading and automatic scoring technology of English translation error text as the fulcrum of the strategy for improving English translation ability in universities. A camera distortion model is established using machine vision technology to eliminate the impact of image distortion caused by translated text and perform image correction. Use median filtering and adaptive Gaussian filtering to eliminate noise in English translation text, and utilize text feature extraction methods to automatically detect translation errors. A translation scoring system is designed and a ternary function model is used to process the translation data and complete the scoring feedback for the translated text. The students of Xinjiang Agricultural University in China were selected as the research subjects to carry out English translation teaching practice. The students in the experimental class outperformed the control class from the second test, and their average scores on the second, third and fourth tests were 2.29, 4.74 and 6.34 higher than those of the control class, and the mean values of 12 learning effect dimensions such as mastery of course content, learning efficiency, and problem-solving ability were higher than those of the control class, while the mean values of the indicators of satisfaction with the teaching of English translation were greater than four.

Keywords

• machine vision technology
• automatic translation scoring
• error detection
• english translation
• 68v35