Exploring the Role of Machine Translation in Translating English Collocations into Arabic: Insights from Student Translators
Bibliographic record
Abstract
Machine Translation (MT) has increasingly become an essential technology in the modern age. MT technology is currently used by many EFL learners as a learning facilitator. They are using MT as an essential tool to assist them in their foreign language learning activities. Several studies have focused on investigating the EFL students’ use of and attitudes towards MT in various EFL learning activities including reading, writing and vocabulary acquisition. However, few studies have been conducted on exploring the EFL learners’ use of MT technology in the translation of collocations, especially in the Arabic context. This study addresses this gap by investigating the impact of MT on the translation of English lexical collocations into Arabic. It presents a corpus of twenty English collocations given to thirty third-year translation students at an Omani university, who utilized an online MT system for their translations. Employing a descriptive, qualitative approach, the study assesses students' strategies and the accuracy of MT-generated equivalents, drawing from translation models by Vinay and Darbelnet (1958) and Newmark (1988). The results indicated that the students were able to generate correct translations for certain collocations when using MT, but there were inaccuracies in the translation of other collocations. The study emphasized the importance of not solely depending on MT because doing so might reduce students' willingness to actively search for the most appropriate translations on their own. This suggests that a balanced approach to using MT and encouraging students to develop their translation skills independently is advisable. Future research can explore the use of Machine Translation in translating collocations in languages beyond Arabic and within different cultural and linguistic contexts.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".