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NADI 2024: The Fifth Nuanced Arabic Dialect Identification Shared Task

Paper · 2024 · Muhammad Abdul-Mageed, Amr Keleg, AbdelRahim Elmadany et al.

Summary

NADI 2024 is the fifth round of a shared-task series on Arabic dialect processing. It addresses a known weakness of earlier editions, which treated dialect identification as choosing exactly one country. Short texts from neighbouring regions are often equally natural in several dialects, and earlier analysis suggested that most "errors" of single-label classifiers are not real mistakes. The organizers therefore offered three subtasks:

  • Subtask 1 is multi-label country-level dialect identification. It is a closed track: systems are trained only on single-label corpora and judged on whether they find every dialect in which a sentence is acceptable.
  • Subtask 2 asks systems to estimate a continuous level of dialectness (ALDi) between 0 and 1.
  • Subtask 3 is dialect-to-MSA machine translation.

New evaluation sets were built by native-speaker annotators. They judged validity per country and added a second annotation layer for ALDi. Twelve of 51 registered teams submitted results. Top scores were modest (50.57 F1, 0.1403 RMSE, 20.44 BLEU), which shows that dialect identification and translation remain open problems.

Key points

  • Participation: 51 teams registered and 12 took part, with 76 valid test submissions. Three teams entered Subtask 1, three entered Subtask 2 and eight entered Subtask 3; eight system papers were accepted.
  • Subtask 1 training data were MADAR-2018, NADI-2020-TWT, NADI-2021-TWT and NADI-2023-TWT.
    • MDID-DEV has 120 sentences labelled for eight countries.
    • MDID-TEST has 1,000 sentences covering nine dialects, including Iraq and Morocco, which were held back from the dev set.
    • Systems choose among 18 country labels.
  • Subtask 2 trained on the AOC-ALDi split. The ALDi-DEV and ALDi-TEST sets reuse the Subtask 1 sentences with an extra layer of dialectness annotation.
  • Three annotators per country judged every sample, not just those geolocated to their own country. Two Egyptian-speaking authors monitored quality and sent high-disagreement items back for re-annotation.
  • Acknowledged limitations:
    • Only 10 dialects are covered, using short tweets.
    • The MT task covers four dialects, with no training data and a single reference.
    • BLEU is used alone.
    • Label aggregation may erase genuine perceptual disagreement.

Interesting ideas

  • I1. Replace one gold label with a set of acceptable labels, and test whether models trained on single-label data can recover all valid options. This turns apparent errors into acceptable alternatives.
  • I2. Hide some categories from the dev set but include them in the test set, which checks whether systems generalize instead of fitting the dev label space.
  • I3. Model "how far from the standard" as a continuous 0–1 score rather than a binary standard/non-standard switch, capturing a continuum between registers.
  • I4. Have every annotator judge all items, then cross-tabulate judged validity against the item's origin to expose systematic overlap between categories.
  • I5. Aggregating annotations reduces noise but can erase real differences in perception, so disagreement itself may be worth keeping as information.

For Mova

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

  • I1 (tree): Evaluate the perceptron parser and gparse against a set of acceptable attachments for genuinely ambiguous sentences, so legitimate alternatives are not counted as LAS errors.
  • I2 (learn): Add held-out frame types or text genres to evaluation as a negative control against overfitting to the development distribution.
  • I3 (tokens): A graded non-standardness score per sentence could tell the tagger and lemmatizer when to trust standard-English morphology and when to fall back to more cautious analyses.
  • I4 (learn): Build a similar matrix of witness-parser agreement against data source to find which constructions are systematically ambiguous rather than noisy.
  • I5 (learn): When building silver data, store the disagreement between witness parsers as an explicit uncertainty signal instead of collapsing it to one majority tree.

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 States539 works · 179,533 citations🎓University of EdinburghEdinburgh · United Kingdom359 works · 77,352 citations🎓University of British ColumbiaVancouver · Canada98 works · 34,805 citations🎓New York University Abu DhabiAbu Dhabi · United Arab Emirates122 works · 4,602 citations🎓Carnegie Mellon University QatarDoha · Qatar47 works · 2,773 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Semitic languages · 2,908 works

Cite

Cite as Muhammad Abdul-Mageed, Amr Keleg, AbdelRahim Elmadany, Chiyu Zhang, Injy Hamed, Walid Magdy, Houda Bouamor and Nizar Habash. 2024. NADI 2024: The Fifth Nuanced Arabic Dialect Identification Shared Task. Proceedings of the Second Arabic Natural Language Processing Conference.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2024.arabicnlp-1.79

@inproceedings{abdulmageed2024nadi,
  title = {{NADI 2024: The Fifth Nuanced Arabic Dialect Identification Shared Task}},
  author = {Muhammad Abdul-Mageed, Amr Keleg, AbdelRahim Elmadany, Chiyu Zhang, Injy Hamed, Walid Magdy, Houda Bouamor and Nizar Habash},
  booktitle = {Proceedings of the Second Arabic Natural Language Processing Conference},
  year = {2024},
  doi = {10.18653/v1/2024.arabicnlp-1.79},
  url = {https://doi.org/10.18653/v1/2024.arabicnlp-1.79}
}

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