On the Role of Orthographic Variations in Building Multidialectal Arabic Word Embeddings
Bibliographic record
Abstract
Dialectal Arabic (DA) is mostly used by over 400 million people across Arab countries as a communication channel on social media platforms, web forums, and daily life.Building Natural Language Processing systems for each DA variant is a challenging issue due to the lack of data and the noisy nature of the available corpora.In this paper, we propose a method to incorporate orthographic features into word embedding mapping methods, inducing a multidialectal embedding space.Our method can be used for both supervised and unsupervised cross-lingual embedding mapping approaches.The core idea of our method is to project the orthographic features into a shared vector space using Canonical Correlation Analysis (CCA).Then, it extends word embedding vectors using the resulting features and learns the multidialectal mapping.The overall obtained results of our proposed method show that our method enhances Bilingual Lexicon Induction of DA by 3.33% and 17.50% compared to state-of-the-art supervised and unsupervised cross-lingual alignment methods, respectively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".