An Innovative Teaching Model of Japanese Language in Colleges and Universities Based on Natural Language Processing Technology
Bibliographic record
Abstract
The traditional Japanese language teaching mode in colleges and universities has been unable to meet the requirements of Japanese language majors in various industries, and colleges and universities should use certain methods to carry out a reasonable reform of the teaching mode of Japanese language majors.Firstly, an error correction model based on UniLM model framework is proposed, using natural language processing technology to extract features, and fine-tuning training for the model after initialization.The model framework based on UniLM+CRF and the seq2seq model framework based on UniLM are built to realize the Japanese text grammar error annotation task and the Japanese text grammar error correction task respectively.Then a multi-task learning error correction method is proposed to integrate the grammar error labeling task and the grammar error correction task, so as to improve the accuracy of the error correction model.Finally, a specific Japanese grammar error correction system architecture is designed, a Japanese language knowledge base is established, and utterance synthesis rules are formulated to realize the innovative teaching of Japanese language in colleges and universities.The average grammatical error correction precision, recall, and F1 value of the model in this paper reached a good level in the students' Japanese composition correction.The error between the average score of teacher correction and the average score of model correction is only 0.19 points, and the related experiments show that the innovative teaching model studied in this paper can effectively improve students' mastery of Japanese syntactic ability.The above data illustrate that the Japanese error correction system based on UmiLM framework designed in this paper has certain application value and can realize the innovation of Japanese language teaching mode.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".