Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation
Bibliographic record
Abstract
Grammatical error correction (GEC) is a wellexplored problem in English with many existing models and datasets.However, research on GEC in morphologically rich languages has been limited due to challenges such as data scarcity and language complexity.In this paper, we present the first results on Arabic GEC using two newly developed Transformer-based pretrained sequence-to-sequence models.We also define the task of multi-class Arabic grammatical error detection (GED) and present the first results on multi-class Arabic GED.We show that using GED information as an auxiliary input in GEC models improves GEC performance across three datasets spanning different genres.Moreover, we also investigate the use of contextual morphological preprocessing in aiding GEC systems.Our models achieve SOTA results on two Arabic GEC shared task datasets and establish a strong benchmark on a recently created dataset.We make our code, data, and pretrained models publicly available.1
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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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".