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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".