The Relationship between Interpreting Anxiety and Learning Motivation Assisted by Long Short-Term Memory Neural Network Algorithms

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Yanling Hong, Hanhui Li


The Long Short-Term Memory (LSTM) neural network algorithm has achieved significant results in natural language processing and speech recognition. This study explores the application of the LSTM algorithm in the study of the relationship between interpreting anxiety and learning motivation. The article mainly uses questionnaire survey method to obtain research data. The participates include students engaged in translation research and professional translators. After statistical analysis of the research data, it was found that there was a significant correlation between translation anxiety and learning motivation (r=-0.62, p<0.001). According to calculation results, the average score is 3.42 and the standard deviation is 0.76. The average score of evaluating the learning enthusiasm of participants through corresponding questionnaires is 4.27, with an SD of 0.92. Further regression analysis of the data was conducted, and the results showed that respondents with low translation anxiety had stronger learning motivation(β=- 0.43, p<0.001). It can be seen that reducing learners' fear of translation can enhance their learning motivation, thereby stimulating their interest in learning. This indicates that reducing translation anxiety may be helpful to improve the learning motivation level of interpreting learners. The study is of great significance for improving the learning enthusiasm of translation learners and reducing translation anxiety and provides a new perspective and research method for future related research.

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