Disregarding Linguistics: A Critical Study of Google Translate's Syntactic/Semantic Errors in Rendering Multiword Units in English to Persian Translations
Bibliographic record
Abstract
Translations rendered by machine translation have been found to be error-prone when translating medical communication language.Issues are especially frequent when Persian is either the source or target language.The source of errors has been attributed to machine translation's utilization of word-for-word translation and disregard for the formulaic nature of language.The aim of this research was, then, to investigate how multiword units (MWUs) of formulaic language (FL) are processed by a commonly used machine translator, Google Translate (GT).To do this, a medical-specific corpus was created to identify non-transparent MWUs.Adopting the framework by Simpson-Vlach and Ellis' (2010) n-gram criteria for MWUs, 20 frequently occurring MWUs were identified in the corpus.A comparison of GT's results with manual translations suggests that GT did not take FL into consideration in 50% of the data.These findings have implications for improving the accuracy of machine translation algorithms and reducing processing time.
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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 | 0.013 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".