Design and Development of BERT-based Neural Network for Evaluating Application-Oriented Japanese Language Talent Cultivation

The development of Japanese language skills for applied, use-oriented situations require precise and thorough evaluation mechanisms. Standardized tests and manual grading tend to dominate traditional approaches to evaluation, failing to reflect the dynamic, contextual, and communicative nature of actual uses of language. These systems are constrained by subjectivity, inadaptability, and minimal insight into deeper semantic meaning. Current methods, such as rule-based and shallow machine learning, exhibit inadequate accuracy for assessing varied linguistic abilities. Overcoming these deficiencies requires a proposed deep learning-based assessment framework employing a Bidirectional Encoder Representations from Transformers (BERT) model. The model efficiently captures contextual semantic attributes, allowing for a deeper and more accurate assessment of learner performance. Comparative evaluation using traditional models indicates that the BERT-based model's performance is far better with an accuracy of 97.4% and an F1-score of 97.2%, outperforming baseline solutions by as much as 8–12%. This proves that the model can identify subtle linguistic characteristics and increase the reliability of evaluation by leveraging deep contextual embeddings and bidirectional analysis.