Research on the Reform of Translation Teaching for English Majors by TBLT under the Background of AI
Bibliographic record
Abstract
With the continuous development of society and the changes of the times, English has gradually become one of a fundamental requirement for talents. As the cradle of cultivating talents, colleges and universities play a pivotal role in educational reform. In the new era, the development of modern information technology demands English teaching to maintain innovation and keep pace with the times. The task-based language teaching approach is used in the translation teaching for English majors in colleges and universities to explore a new mode of English teaching. In teaching, by allowing students to complete the corresponding target tasks, develop students' ability to comprehensively use information, and promote the effective cultivation of students' English translation ability, thereby improving students' English cross-cultural level and translation ability. College English teaching should prioritize the cultivation of students' practical translation ability. Therefore, based on the characteristics of task-based language teaching, artificial intelligence technology is introduced into English translation teaching for English majors to assist in completing tasks using intelligent translation, so as to realizing a new model of student-centered English teaching. Taking English translation teaching as an example, this paper discusses how to reform the translation teaching for English majors in colleges and universities under the new situation of artificial intelligence application in the field of translation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".