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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.009 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".