Self-calibration system for pragmatic failure in English-Chinese translation based on big data

Abstract

Aiming at the problems of long time, high energy consumption and low accuracy of the current English-Chinese translation pragmatic self-calibration system, a design method of English-Chinese translation pragmatic self-calibration system based on big data is proposed. In the hardware part of the system, the framework of the pragmatic error self-calibration system is designed. The speech is converted into digital signals by the speech recognition module, and the recognised digital signals are translated into Chinese by the functions in the translation module. In the software part of the system, the sample risk minimisation algorithm is adopted to keep the loss function in the sample minimum, and the calibration model is built according to the linear search and feature selection results. The experimental results show that the energy consumption coefficient of the designed system varies from 0 to 1.5. The average calibration accuracy is 95% and the calibration accuracy is high.