Abstract
With the deepening of globalization and the increasing amount of information in international communication, requirements for accuracy in interpretation of figures are more demanding. Training methods in interpretation of figures, however, are not efficient enough for interpreters to cope with the challenges. They still make mistakes in interpretation of large integers, fractions and percentages. The types of errors include omission, syntactic error and lexical error. In this paper, machine learning based text categorization technology is used to accurately categorize a large number of texts and provide high-quality training materials for interpreters. Results show that training the interpreters with categorized texts has greatly improved the accuracy, familiarity and sensitivity in interpretation of figures. In the era of artificial intelligence, problems in interpretation also need to be solved by artificial intelligence. In the future, a large number of artificial intelligence technologies similar to machine-learning-based text categorization technology will be inevitably adopted in the field of interpretation.
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Acknowledgement
This study was financially supported by the Undergraduate Innovation Training Project of Guangdong University of Foreign Studies in 2019 (NO. 201911846007).
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Huang, W., He, Z., Li, X. (2020). The Application of Text Categorization Technology in Adaptive Learning System for Interpretation of Figures. In: Kim, J., Geem, Z., Jung, D., Yoo, D., Yadav, A. (eds) Advances in Harmony Search, Soft Computing and Applications. ICHSA 2019. Advances in Intelligent Systems and Computing, vol 1063. Springer, Cham. https://doi.org/10.1007/978-3-030-31967-0_15
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DOI: https://doi.org/10.1007/978-3-030-31967-0_15
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