Evaluation of Google Image Translate in Rendering Arabic Signage into English
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When people travel to another country for work or leisure, they regularly need a medium to help them understand the written messages in other languages. Google Translate offers a new service: translating the content of images (texts) instantly and freely into 100 languages powered by the Neural Machine Translation approach (NMT). In this vein, the current research paper attempts to evaluate the accuracy of Google Image Translate service in rendering the texts printed on Arabic signage: banners and road and shop signs from Arabic into English. Besides, it aims to identify the capacity of Google Translate in rendering Arabic signage into English effectively without the help of human translators. The paper adopts the Linguistic Error Analysis Framework of Costa et al. (2015) in analyzing the output of Google image service in terms of orthography, grammar, lexis, and semantics. The paper shows that Google Translate made the following errors while rendering the content of images into English: mistranslation, omission, additions, wrong choice, misordering, subject-verb disagreement, and semantic errors. In conclusion, the Google Image Translate service helps the users configure the gist of the image. However, a human translator is still needed since MT may not provide an adequate and effective translation as humans do.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it