sis 24(3):

Research Article

Research on Automatic Scoring Method of Intelligent Translation System Based on TSO Optimized LSTM Networks

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  • @ARTICLE{10.4108/eetsis.4858,
        author={Wei Li},
        title={Research on Automatic Scoring Method of Intelligent Translation System Based on TSO Optimized LSTM Networks},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={3},
        publisher={EAI},
        journal_a={SIS},
        year={2024},
        month={2},
        keywords={automatic scoring for language translation systems, long and short-term memory networks, triangle search optimization algorithm, automatic scoring feature analysis},
        doi={10.4108/eetsis.4858}
    }
    
  • Wei Li
    Year: 2024
    Research on Automatic Scoring Method of Intelligent Translation System Based on TSO Optimized LSTM Networks
    SIS
    EAI
    DOI: 10.4108/eetsis.4858
Wei Li1,*
  • 1: Jiangsu Maritime Institute
*Contact email: 13952053527@163.com

Abstract

INTRODCTION: The study of automatic marking methods in the Department of Language Translation is conducive to the fairness and rationality of marking by examining the comprehensive level of the students' language, as well as sharing the objectivity and pressure of the marking teachers in marking the scripts. OBJECTIVES: Aiming at the current automatic scoring methods of translation systems, which have the problems of not considering the global nature of influence features and low precision. METHODS: This paper proposes an automatic scoring method for translation system based on intelligent optimization algorithm to improve the deep network. First, by analyzing the language translation scoring problem, selecting the key scoring influencing factors and analyzing the correlation and principal components; then, improving the long and short-term memory network through the triangle search optimization algorithm and constructing the automatic scoring model of the translation system; finally, the high efficiency of the proposed method is verified through the analysis of simulation experiments. RESULTS: The proposed method is effective and improves the accuracy of the scoring model. CONCLUSION: solves the problem of inefficient scoring in the automatic scoring method of the translation system.