Bibliographic record
Abstract
Translation from/to Arabic has been widely studied recently. This study focuses on the translation of Arabic as a source language (SL) to Malay as a target language (TL). The proposed prototype will be conducted to map the SL ”meaning”with the most equivalent translation in the TL. In this paper, we will investigate Arabic-Malay Machine Translation features (i.e., syntactic, semantic, and morphology), our proposed method aims at building a robust lexical Machine Translation prototype namely (AMMT). The paper proposes an ongoing research for building a successful Arabic-Malay MT engine. Human judgment and bleu evaluation have been used for evaluation purposes, The result of the first experiment prove that our system(AMMT) has outperformed several well-regarded MT systems by an average of 98, while the second experiment shows an average score of 1-gram, 2-gram and 3-gram as 0.90, 0.87 and 0.88 respectively. This result could be considered as a contribution to the domain of natural language processing (NLP).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".