The Role of Dictionaries in Translation Performance: A Case of English to Persian Translation
Bibliographic record
Abstract
This study tries to see if the application of dictionaries in translation tasks can improve the quality of translation. In order to reach the purpose, this study investigates the issue both quantitatively and qualitatively in two phases. In the opening phase of the project a questionnaire was given to 230 Iranian translators in seven Iranian state universities to investigate the type of monolingual dictionaries they use while translating informative texts like news reports. In the main phase of the study, three groups of translators with different types of dictionaries- hardcover, computer software, and mobile dictionaries- were selected and given the task of translating three news texts from English to Persian, and their translations were assessed in terms of the accuracy of the words and expressions of the source text and the speed of the job. Results indicated that translators using mobile dictionaries rendered the texts more accurately and much faster than the other two groups. Translators using computer software occupied the second rank, and hardcover dictionary users, bringing up the rear, were the last group to finish the job. This study shows how mobile dictionaries can provide help that meets the needs of translators when translating informative texts.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".