Investigating the Contrastive Grammar Problems EFL learners Encounter in Arabic-English Translation
Bibliographic record
Abstract
The continuous development of science, culture, and technology has increased the need for translation in modern life. The importance of the translation process should not be ignored because it always involves the translation texts from one language into another. English and Arabic share some similarities and differences, so we must keep this in mind. Many linguistic problems arise during the translation process. Accordingly, this study aims to identify the grammatical problems that EFL learners face when translating from Arabic into English as a target language; design and implement a test to find out such grammatical problems that fourth year students face when translating from Arabic into English; and provide solutions for these problems that Iraqi EFL learners encounter in Arabic-English translation. This study hypothesizes that the interference of the mother tongue and literal translation lead to incorrect translation; the most frequent problems in Arabic-English translation are those related to tenses, nominal vs verbal sentences, and word order; and problems that learners do not suffer from in Arabic-English translation are adjectives and negation. The test is taken by the morning and evening participants at the Dept. of English, College of Education for Humanities, University of Thi-Qar, 100 students (morning and evening) take the test throughout the academic year (2022-2023). The test consists of 20 Arabic items and the students are required to translate them into English. The research concludes that the greatest problematic areas for both morning and evening participants are: the past participle, the conditional sentences, the nominal vs. Verbal sentences, the word classes, and the tenses.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".