What Skills Do Student Interpreters Need to Learn in Sight Translation Training?
Bibliographic record
Abstract
Although sight translation is widely taught in interpreter education and practicsed in the field, there has been a dearth of studies on sight translation. This paper presents the preliminary findings of a pilot study comparing six student interpreters and three professional interpreters’ sight translation of an English speech text into Korean, which is their A language. This paper examines their sight translation performances in terms of accuracy, target language expressions and delivery qualities. The results indicate that student interpreters need to further develop their reading skills to accurately understand the source text and distinguish key ideas from ancillary ideas. The data analysis also reveals that student interpreters need to make conscious efforts to distance themselves from the source language form and develop translation skills to avoid literal translations. These findings have pedagogical implications for sight translation training. This paper discusses condensation strategy as an effective method to enhance delivery and target language qualities. Finally, this paper calls for further research on this under-researched component in the interpreting curriculum.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".