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NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task

Paper · 2022 · Muhammad Abdul-Mageed, Chiyu Zhang, AbdelRahim Elmadany et al.

Summary

NADI 2022 is the third edition of a shared task on Arabic dialects. It targets fine-grained variation at the country level in short social media posts. The organisers set two subtasks. Subtask 1 asks systems to recognise which of 18 national dialects a tweet comes from. Subtask 2 asks for sentiment (positive, negative, neutral) of tweets from 10 countries, which may be MSA or dialectal. Subtask 1 reuses the NADI 2021 splits and adds a second, newly annotated test set. That test set covers only a subset of the countries, and how many was not disclosed to teams. Subtask 2 comes with a new manually labelled dataset, which the authors present as the first to support sentiment work across 10 Arabic dialects. Submissions were scored on CodaLab under shared restrictions so that comparisons were fair. Of 41 registered teams, 21 submitted. Most fine-tuned Arabic Transformer models. The top scores were F1 27.06 for dialect identification and 75.16 for sentiment. Both problems therefore remain far from solved, and the results give a standard benchmark for future work.

Key points

  • 41 teams registered and 21 made 105 valid submissions: 19 teams in Subtask 1 and 10 in Subtask 2. 15 system papers were accepted.
  • Subtask 1 is evaluated on two test sets: Test-A, the NADI 2021 test set over 18 countries, and Test-B, a new set covering k = 10 countries, with k hidden from participants. The official score is the macro-F1 averaged over both test sets.
  • Subtask 2 uses tweets collected in 2018 from 10 countries with three sentiment labels. The official metric is F1 averaged over only the positive and negative classes (F1_NP).
  • The winning team, rematchka, combined MARBERT, AraBERT and AraGPT2 with prompting and added linguistic features. The leader on Test-B, UniManc, used MARBERT with a sampling scheme favouring under-represented classes.
  • The organisers also released 10M unlabelled tweets. UniManc found that further pre-training MARBERT on them helped sentiment but not dialect identification. Other techniques included adapters with data augmentation (mtu_fiz, ISL_AAST), and six teams also used classical ML such as SVM and Naive Bayes.

Interesting ideas

  • I1. A second test set covers an undisclosed subset of the training labels, which checks whether systems cope when the label distribution at test time shrinks unexpectedly.
  • I2. The sentiment metric averages F1 over the polar classes only, so a system gains nothing by predicting the majority "neutral" class.
  • I3. An old test set is kept alongside a fresh one, which allows comparison with past editions while also exposing overfitting to the known benchmark.
  • I4. Additional in-domain pre-training helped one task but not the other, so the benefit of extra unlabelled data depends on the task.
  • I5. Sampling that boosts rare classes was a key ingredient for the strongest Test-B system.

For Mova

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

  • I1 (learn): Evaluate taggers and readers on held-out sets whose label inventory is secretly narrowed, as a negative control against models that rely on label priors.
  • I2 (answer): Score readers mainly on informative answer classes, not on "unknown/none" defaults, so that hedging does not inflate results.
  • I3 (tree): Keep the frozen treebank test split and add a small newly annotated set in another English variety, checking that LAS gains carry over to the new set.
  • I4 (learn): Measure the effect of silver data separately for each stage instead of assuming that more data helps the whole pipeline.
  • I5 (frames): Oversample rare VerbNet roles and speech-act types when training frame assignment so that they are not swamped by frequent agent/patient cases.

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🎓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, Chiyu Zhang, AbdelRahim Elmadany, Houda Bouamor and Nizar Habash. 2022. NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task. Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2022.wanlp-1.9

@inproceedings{abdulmageed2022nadi,
  title = {{NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task}},
  author = {Muhammad Abdul-Mageed, Chiyu Zhang, AbdelRahim Elmadany, Houda Bouamor and Nizar Habash},
  booktitle = {Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)},
  year = {2022},
  doi = {10.18653/v1/2022.wanlp-1.9},
  url = {https://doi.org/10.18653/v1/2022.wanlp-1.9}
}

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