Automatic identification of arabic dialects in social media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modern Standard Arabic (MSA) is the formal language in most Arabic countries. Arabic Dialects (AD) or daily language differs from MSA especially in social media communication. However, most Arabic social media texts have mixed forms and many variations especially between MSA and AD. This paper aims to bridge the gap between MSA and AD by providing a framework for AD classification using probabilistic models across social media datasets. We present a set of experiments using the character n-gram Markov language model and Naive Bayes classifiers with detailed examination of what models perform best under different conditions in social media context. Experimental results show that Naive Bayes classifier based on character bi-gram model can identify the 18 different Arabic dialects with a considerable overall accuracy of 98%. This work is a first-step towards an ultimate goal of a translation system from Arabic to English and French, within the ASMAT project
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it