Neural Machine Translation: Fine-Grained Evaluation of Google Translate Output for English-to-Arabic Translation
Bibliographic record
Abstract
The neural machine translation (NMT) revolution is upon us. Since 2016, an increasing number of scientific publications have examined the improvements in the quality of machine translation (MT) systems. However, much remains to be done for specific language pairs, such as Arabic and English. This raises the question whether NMT is a useful tool for translating text from English to Arabic. For this purpose, 100 English passages were obtained from different broadcasting websites and translated using NMT in Google Translate. The NMT outputs were reviewed by three professional bilingual evaluators specializing in linguistics and translation, who scored the translations based on the translation quality assessment (QA) model. First, the evaluators identified the most common errors that appeared in the translated text. Next, they evaluated adequacy and fluency of MT using a 5-point scale. Our results indicate that mistranslation is the most common type of error, followed by corruption of the overall meaning of the sentence and orthographic errors. Nevertheless, adequacy and fluency of the translated text are of acceptable quality. The results of our research can be used to improve the quality of Google NMT output.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".