 Automatic Scoring Model of Japanese Interpretation Based on Semantic ScoringQin Wang and Hengchang Jing Mathematical Problems in Engineering, 2022, vol. 2022, 1-7 Abstract: In order to improve the current level of Japanese teaching and the difficulty of non-standard Japanese spoken language, the author proposes a method for the study of the automatic scoring model of Japanese ants for scoring. The author introduces a semantic scoring model that integrates the long short-term memory neural network and self-attention mechanism, which can be applied to keyword scoring and sentence semantic scoring. The scoring principle of the model is as follows: firstly, extract the word and sentence features and represent them in a vectorized form, then use a bidirectional long short-term memory neural network to optimize the feature vector, and then use the self-attention mechanism to obtain the semantic features of the word or sentence. Finally, the semantic score is calculated by a simple neural network. Experiments show that compared with the semantic scoring model based on a stretchable recursive autoencoder that performs better in semantic scoring, the average correlation between this model and the original score is 0.444; the lowest rate of agreement with the original score is 95%; and the highest rate of agreement with adjacent ones is 74%. The automatic scoring model for Japanese interpreting with semantic scoring is proved to be practical and has excellent results. Date: 2022 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:3299549 DOI: 10.1155/2022/3299549 Access Statistics for this article More articles in Mathematical Problems in Engineering from Hindawi |
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