---
title: "Arabic Dialect Identification with a Few Labeled Examples Using"
description: "The authors attach a Semi-Supervised GAN (SS-GAN), following the GAN-BERT design, to the Arabic… 18 references, cited by 1 paper here."
authors: ["Mahmoud Yusuf", "Marwan Torki", "Nagwa El-Makky"]
year: 2022
venue: "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)"
source: "https://aclanthology.org/2022.aacl-main.16/"
doi: "10.18653/v1/2022.aacl-main.16"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yusuf-2022-arabic-dialect-identification-few-labeled/"
---
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# Arabic Dialect Identification with a Few Labeled Examples Using Generative Adversarial Networks

**Authors:** Mahmoud Yusuf, [Marwan Torki](https://md.research.elamur.ai/free/en/authors/marwan-torki/) and [Nagwa El-Makky](https://md.research.elamur.ai/free/en/authors/nagwa-el-makky/) · **Year:** 2022 · **Venue:** [Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/aacl/) ([AACL 2022](https://md.research.elamur.ai/free/en/venues/aacl/2022/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.aacl-main.16/) · [PDF](https://aclanthology.org/2022.aacl-main.16.pdf) · [DOI 10.18653/v1/2022.aacl-main.16](https://doi.org/10.18653/v1/2022.aacl-main.16) · [OpenAlex](https://openalex.org/W4404791720)

**Citations:** 5 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/yusuf-2022-arabic-dialect-identification-few-labeled/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper addresses Arabic dialect identification when only a handful of labeled examples exist for each dialect. Labeled data for this task is scarce, unevenly distributed across countries, and often noisy, because some corpora assign labels from a user's location. The authors attach a Semi-Supervised GAN (SS-GAN), following the GAN-BERT design, to the Arabic pretrained encoders ARBERT and MARBERT. A generator produces fake sentence vectors from noise. A discriminator then has to separate real from fake while also predicting the dialect class, and it learns from both labeled and unlabeled examples. They also try a 2-stage schedule: first train with the GAN attached, then remove it and keep fine-tuning the plain encoder. Experiments use tiny random subsets (0.01%–10%) of four corpora: QADI, NADI 2021, ArSarcasm and AOC. In the smallest-data regimes, macro-F1 often improves two- to four-fold over plain fine-tuning, and training converges faster. As the labeled data grows, the gap disappears. The result matters for any low-resource variety where raw text is cheap but annotation is expensive.

## Key points
- Base models are ARBERT and MARBERT. The SS-GAN extension is modeled on GAN-BERT (Croce et al., 2020), and the authors report it as the first use of this setup for Arabic dialect identification.
- The four corpora are QADI (367,353 train / 3,304 test, 18 country dialects, clean and fairly balanced), NADI 2021 (imbalanced, with 4 classes covering over half the data), ArSarcasm (mostly MSA/Egyptian) and AOC (86,542 train / 10,812 test, 4 classes).
- Selected GAN-MARBERT vs MARBERT macro-F1 results:
  - NADI at 1% (210 examples): about 8 vs 2.8.
  - AOC at 0.02% (17 examples): over 26 vs 17.
  - AOC at 0.05% (184 examples): 47 vs 18.
  - QADI at 0.01–0.02% (37–74 examples): more than four times the baseline.
- The 2-stage setup uses 5 epochs with the GAN and 5 without, learning rate 2e-5 and sequence length 40. Its results versus plain fine-tuning:
  - ArSarcasm at 1%: 32 vs 20.5 (ARBERT).
  - NADI at 1%: 10.9 vs 7.2.
  - QADI at 0.01%: 7.8 vs 2.2.
  - AOC at 0.05%: 43.9 vs 18.5.
- Gains shrink or vanish with more labels. For example, ArSarcasm at 5% gives 45.7 for the 2-stage setup vs 47 for ARBERT.

## Interesting ideas
- The discriminator gets one extra "fake" class on top of the real labels, so unlabeled real text still trains it by teaching what real input looks like.
- The adversarial part is temporary scaffolding: it helps early training and is then removed, so inference costs nothing extra.
- The authors explain why standard augmentation fails here: word swaps lose meaning, dialect synonym lists barely exist, and back-translation drifts toward the standard variety (MSA) and erases the very feature being classified.
- The evaluation sweeps learning curves down to a few dozen labeled examples, which shows where an extension helps and where it stops mattering.
- The paper warns that labels taken from metadata (user location) introduce systematic noise that limits generalization.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): A parser or tagger head could use this to draw signal from large raw English corpora while the gold set stays tiny, as a complement to silver data from witness parsers.
- **I2** (tree): gparse could warm up with an auxiliary adversarial or unlabeled-data module for a few epochs, then drop it and ship the unchanged parser.
- **I3** (back): When the text → graph → text compressor or translation is used to create training data, check that it keeps register and dialect markers and does not normalize them away.
- **I4** (learn): Report each new component at several training-set fractions, with the plain baseline as a control, instead of a single full-data score.
- **I5** (learn): Treat any label taken from document source or metadata as a weak witness that must agree with others before it enters silver data.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Alexandria University](https://md.research.elamur.ai/free/en/universities/alexandria-university/) — University, Alexandria, Egypt · 42 works · 287 citations

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## Cite

Cite as: Mahmoud Yusuf, Marwan Torki and Nagwa El-Makky. 2022. Arabic Dialect Identification with a Few Labeled Examples Using Generative Adversarial Networks. Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{yusuf2022arabic,
  title = {{Arabic Dialect Identification with a Few Labeled Examples Using Generative Adversarial Networks}},
  author = {Mahmoud Yusuf, Marwan Torki and Nagwa El-Makky},
  booktitle = {Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2022},
  doi = {10.18653/v1/2022.aacl-main.16},
  url = {https://doi.org/10.18653/v1/2022.aacl-main.16}
}
```

- [← previous AraBERT Model for Propaganda Detection](https://md.research.elamur.ai/free/en/papers/sharara-2022-arabert-model-propaganda-detection/)
- [next → AraProp at WANLP 2022 Shared Task: Leveraging Pre-Trained Language Models for Arabic Propaganda Detection](https://md.research.elamur.ai/free/en/papers/singh-2022-araprop-wanlp-2022-shared-task/)
- [↑ back to papers of 2022](https://md.research.elamur.ai/free/en/papers/2022/page/7/)

## References

18 references, 15 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Ahmed Abdelali, Hamdy Mubarak, Younes Samih, Sabit Hassan, and Kareem Darwish. 2021. [QADI: Arabic dialect identification in the wild](https://md.research.elamur.ai/free/en/papers/abdelali-2021-qadi-arabic-dialect-identification-wild/). In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 1–10, Kyiv, Ukraine (Virtual). Association for Computational Linguistics.
2. Muhammad Abdul-Mageed, AbdelRahim Elmadany, et al. 2021a. [Arbert & marbert: Deep bidirectional transformers for arabic](https://md.research.elamur.ai/free/en/papers/abdul-mageed-2021-arbert-marbert-deep-bidirectional-transformers/). In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 7088–7105. [↗](https://doi.org/10.48550/arxiv.2101.01785)
3. Muhammad Abdul-Mageed, Chiyu Zhang, Houda Bouamor, and Nizar Habash. 2020. [Nadi 2020: The first nuanced arabic dialect identification shared task](https://md.research.elamur.ai/free/en/papers/mageed-2020-nadi-2020-first-nuanced-arabic/). In Proceedings of the Fifth Arabic Natural Language Processing Workshop, pages 97–110. [↗](https://doi.org/10.48550/arxiv.2010.11334)
4. Muhammad Abdul-Mageed, Chiyu Zhang, AbdelRahim Elmadany, Houda Bouamor, and Nizar Habash. 2021b. [Nadi 2021: The second nuanced arabic dialect identification shared task](https://md.research.elamur.ai/free/en/papers/mageed-2021-nadi-2021-second-nuanced-arabic/). In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 244–259. [↗](https://doi.org/10.48550/arxiv.2103.08466)
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8. Houda Bouamor, Sabit Hassan, and Nizar Habash. 2019. [The madar shared task on arabic fine-grained dialect identification](https://md.research.elamur.ai/free/en/papers/bouamor-2019-madar-shared-task-arabic-fine/). In Proceedings of the Fourth Arabic Natural Language Processing Workshop, pages 199– 207.
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*Updated 2 November 2026*