The Frequency Taxonomy of Syntactico-Morphological Errors in Persian-English Translation Based on Contrastive Analysis & Error Analysis
Bibliographic record
Abstract
This study aims to provide a linguistic taxonomy of frequent errors in Persian to English translation. It also proposes the most frequent and the least frequent errors among EFL students. Translation of Persian to English makes Iranian translators confront the problems such as Orthographic errors, Phonological errors, Lexico-Semantic errors and /or Syntactico-Morphological errors. The main concern of this study would be on the Syntactico-Morphological errors. Error Analysis is a procedure used by both researchers and teachers which involves collecting samples of learner language, identifying the errors in the sample, describing these errors, classifying them according to their nature and causes, and evaluating their seriousness. The researchers conducted a translation project on 500 EFL undergraduate university students in Teaching English as a Foreign Language, Translating and English Language and Literature field of studies. Students were asked to translation 30 sentences from Persian to English. After that, the researchers tried to rank and categorize them according to Contrastive Analysis and Error Analysis. At the content and context levels, there may be several shared properties between SL and TL equivalents which are connotatively motivated while at the formal level the lexical differences can be problematic. The researchers hypothesize that in errors in use of tenses and in use of articles are the most frequent errors. On the other hand, errors the use of plural morpheme are the least ones. The research is going to help to pinpoint the potential problematic errors and provide some pedagogical guidelines for teachers, syllabus designers and test constructors.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".