English/Arabic/English Machine Translation: A Historical Perspective
Bibliographic record
Abstract
This paper examines the history and development of Machine Translation (MT) applications for the Arabic language in the context of the history and machine translation in general. It starts with a discussion of the beginnings of MT in the US and then, depending on the work of MT historians, surveys the decline of the work on MT and drying up of funding; then the revival with globalization, development of information technology and the rising needs for breaking the language barriers in the world; and last on the dramatic developments that came with the advances in computer technology. The paper also examined some of the major approaches for MT within a historical perspective. The case of Arabic is treated along the same lines focusing on the work that was done on Arabic by Western research institutes and Western profit motivated companies. Special attention is given to the work of the one Arab company, Sakr of Al-Alamiyya Group, which was established in 1982 and has seriously since then worked on developing software applications for Arabic under the umbrella of natural language processing for the Arabic language. Major available software applications for Arabic/English Arabic MT as well as MT related software were surveyed within a historical framework.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".