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NADI 2023: The Fourth Nuanced Arabic Dialect Identification Shared Task

Paper · 2023 · Muhammad Abdul-Mageed, AbdelRahim Elmadany, Chiyu Zhang et al.

Summary

NADI 2023 is the fourth round of a shared task on Arabic dialects. Arabic is really a family of varieties, and some of them are not mutually intelligible. The organizers set up three subtasks under one common evaluation:

  • Subtask 1: country-level dialect identification on a new Twitter collection, TWT-2023, covering 18 dialects.
  • Subtask 2 (closed track): translation from four dialects into MSA, using only the official training data.
  • Subtask 3 (open track): the same translation task, but any public extra data is allowed.

For the translation subtasks the organizers annotated and released a new parallel dataset. They describe it as the first shared task on MT for these four dialects. Most of the 18 participating teams fine-tuned pretrained models: encoder models such as MARBERT for identification, and encoder-decoder models such as AraT5 for translation. Many teams also used ensembles.

The best scores were 87.27 F1 for identification and modest BLEU for translation. Translation was clearly weaker, largely because dialect–MSA parallel data is scarce. The task matters because it gives the community standardized benchmarks for dialect processing in a low-resource setting.

Key points

  • Participation: 58 teams registered, 18 submitted (76 valid test-phase runs), and 13 system papers were accepted. Team counts were 16 for Subtask 1, 5 for Subtask 2 and 3 for Subtask 3.
  • Subtask 1 data: TWT-2023, about 23.4K tweets across 18 country dialects. Teams could also train on the NADI-2020, NADI-2021 and MADAR training splits.
  • MT subtasks cover Egyptian, Emirati, Jordanian and Palestinian Arabic, translated into MSA.
  • Winning scores:
    • Subtask 1: NLPeople, 87.27 F1.
    • Subtask 2: UniManc, 14.76 BLEU.
    • Subtask 3: UniManc, 21.10 BLEU.
  • Stated limitations:
    • Some countries (Comoros, Djibouti, Mauritania, Somalia) were left out for lack of good data.
    • The texts are short tweets.
    • MADAR is commissioned rather than naturally occurring text.
    • The MT datasets are small.

Interesting ideas

  • I1. NLPeople retrieved similar labeled training texts and attached them, with their gold labels, to each input, giving the model nearby examples as context.
  • I2. Running a closed track (official data only) next to an open track (any public data) separates gains from modeling and gains from extra resources.
  • I3. Helsinki-NLP made synthetic parallel data in the reverse direction: character-level statistical MT turned plentiful standard-language text into dialect-like text.
  • I4. UniManc used GPT-3.5-turbo to translate unlabeled in-domain samples into extra training pairs for the open track.
  • I5. rematchka added a supervised contrastive loss during fine-tuning, which pulls same-class examples together in representation space.

For Mova

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

  • I1 (tree): gparse could receive the gold UD analyses of the closest treebank sentences as extra context, which also gives an explainable reason for each attachment decision.
  • I2 (learn): we could report every evaluation twice, once with in-house data and once with external resources, so the source of an improvement stays clear.
  • I3 (tree): converting standard UD treebank sentences into informal or dialectal English variants while keeping their trees would yield silver training data for robust parsing.
  • I4 (learn): an LLM teacher tier could label in-domain raw text, with witness parsers checking agreement before the output is accepted as silver data.
  • I5 (tokens): a contrastive objective could sharpen the separation of confusable tags or morphological classes in the tagger's embeddings.

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

🎓Carnegie Mellon UniversityPittsburgh · United States365 works · 158,048 citations🎓University of British ColumbiaVancouver · Canada46 works · 33,673 citations🎓New York University Abu DhabiAbu Dhabi · United Arab Emirates49 works · 2,404 citations🎓Mohamed bin Zayed University of Artificial IntelligenceAbu Dhabi · United Arab Emirates47 works · 4,456 citations🎓Carnegie Mellon University QatarDoha · Qatar13 works · 1,947 citations

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 works

🧭 Topics 1Research subjects and languages the work is about.Semitic languages · 1,484 works

Cite

Cite as Muhammad Abdul-Mageed, AbdelRahim Elmadany, Chiyu Zhang, El Moatez Billah Nagoudi, Houda Bouamor and Nizar Habash. 2023. NADI 2023: The Fourth Nuanced Arabic Dialect Identification Shared Task. Proceedings of ArabicNLP 2023.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.arabicnlp-1.62

@inproceedings{abdulmageed2023nadi,
  title = {{NADI 2023: The Fourth Nuanced Arabic Dialect Identification Shared Task}},
  author = {Muhammad Abdul-Mageed, AbdelRahim Elmadany, Chiyu Zhang, El Moatez Billah Nagoudi, Houda Bouamor and Nizar Habash},
  booktitle = {Proceedings of ArabicNLP 2023},
  year = {2023},
  doi = {10.18653/v1/2023.arabicnlp-1.62},
  url = {https://doi.org/10.18653/v1/2023.arabicnlp-1.62}
}

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