The Use of English Collocations in Written Translation – A Case of University English-Majored Students
Bibliographic record
Abstract
The current study attempted to investigate English collocations used in written translation among fourth-year English majors at a university. The participants included forty-one fourth-year English-majored students and three translation teachers who are teaching English – Vietnamese translation courses in the university. To gather data, the researchers used two instruments: a test of English collocations including five types of collocations: an adjective going with a noun, a verb going with a noun, a noun going with a verb, a noun going with a noun and a verb going with an adverb. After data analysis, it revealed that just slightly over half of the student participants were able to find the correct collocations in written translation. Furthermore, a noun going with a verb and a noun going with a noun are the two main lexical errors made by most of the students. The interview was also used to seek translation teachers’ perspectives on students’ ability to translate texts in general and strategies of translation in particular. They perceived that most of their students were not aware of collocations in written translation and still had difficulties in both grammatical and lexical collocations. Finally, some recommendations on improving English collocations in written translation were also made based on these findings.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".