Design and Implementation of an Automatic Evaluation System for English-Chinese Interpretation Based on Artificial Intelligence

Abstract

With the development of globalization, English-Chinese interpretation plays an increasingly important role in international communication. The aim of this study is to design and implement an artificial intelligence-based automatic evaluation system for English-Chinese interpreting, and to provide an objective and efficient tool for the evaluation of interpretation quality. By using a hybrid model comprising a constitutional neural network (CNN) and a long short-term memory network (LSTM), the system can effectively deal with complex linguistic phenomena in interpreting content. In this study, through comprehensive data collection and re-processing, high-quality data was obtained as input for model training. System tests show that the model achieves good results in terms of functionality, performance and user experience, especially in regard to semantic accuracy and fluency. However, the study also has limitations, such as the diversity of data samples and the processing power in complex contexts. Future research will focus on expanding the datasets and further optimizing the algorithm to improve the generalization ability and accuracy of the system. In general, this study provides a new technical perspective for the field of automatic assessment of English-Chinese interpreting, and provides a valuable reference for related research.

Keywords: artificial intelligence, English-Chinese interpretation, automatic evaluation system, convolution neural network, long short-term memory network.

Cite As

F. Ren, "Design and Implementation of an Automatic Evaluation System for English-Chinese Interpretation Based on Artificial Intelligence", Engineering Intelligent Systems, vol. 33 no. 3, pp. 249-261, 2025.