---
title: "NADI 2020 — paper by Abdul-Mageed et al. (2020)"
description: "NADI 2020 is a shared task on recognizing where an Arabic tweet's author comes from based on… 46 references, cited by 102 papers here."
authors: ["Muhammad Abdul-Mageed", "Chiyu Zhang", "Houda Bouamor", "Nizar Habash"]
year: 2020
venue: "Proceedings of the Fifth Arabic Natural Language Processing Workshop"
source: "https://aclanthology.org/2020.wanlp-1.9/"
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/mageed-2020-nadi-2020-first-nuanced-arabic/"
---
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# NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task

**Authors:** [Muhammad Abdul-Mageed](https://md.research.elamur.ai/free/en/authors/muhammad-abdul-mageed/), [Chiyu Zhang](https://md.research.elamur.ai/free/en/authors/chiyu-zhang/), [Houda Bouamor](https://md.research.elamur.ai/free/en/authors/houda-bouamor/) and [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/) · **Year:** 2020 · **Venue:** [Proceedings of the Fifth Arabic Natural Language Processing Workshop](https://md.research.elamur.ai/free/en/venues/wanlp/) ([WANLP 2020](https://md.research.elamur.ai/free/en/venues/wanlp/2020/)) · **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/2020.wanlp-1.9/) · [PDF](https://aclanthology.org/2020.wanlp-1.9.pdf)

**Citations:** [102](https://md.research.elamur.ai/free/en/papers/mageed-2020-nadi-2020-first-nuanced-arabic/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
NADI 2020 is a shared task on recognizing where an Arabic tweet's author comes from based on dialect. It has two subtasks: assigning one of 21 Arab countries (Subtask 1) and one of 100 provinces (Subtask 2). Earlier finer-grained work, such as MADAR, relied on commissioned parallel sentences. NADI instead uses naturally occurring Twitter text and goes below the country level with a large province label set, which the organizers describe as a first.

The organizers built a new labeled tweet corpus with fixed TRAIN/DEV/TEST splits. No user appears in more than one split. They also released IDs for 10M unlabeled tweets and ran scoring on CodaLab, with macro-averaged F1 as the official metric.

Results show the task is hard. The best country-level system (Mawdoo3-AI) reached 26.78% macro-F1, and the best province-level system (BERT-NGRAMS) reached only 6.39%. Most leading teams used Transformers, often combined with extra pre-training on the unlabeled tweets or with self-training. The benchmark sets a baseline for studying micro-geographic language variation.

## Key points
- Labeled data size and splits:
  - 30,957 tweets in total: 21,000 TRAIN, 4,957 DEV and 5,000 TEST.
  - The data covers 21 countries and 100 provinces.
  - Classes are very unbalanced. Egypt makes up about 21% of the data, while countries such as Bahrain, Djibouti and Mauritania each account for under 1%.
- Participation:
  - Subtask 1 received 47 submissions from 18 teams.
  - The Subtask 2 results table lists 4 teams. The abstract gives a different team count.
  - 14 system description papers were accepted.
- Top Subtask 1 scores (macro-F1): Mawdoo3-AI 26.78% (accuracy 42.86%), BERT-NGRAMS 25.99%, Arabic Processors 23.26%. The organizers' stronger baseline scored 13.32%.
- Top Subtask 2 scores (macro-F1): BERT-NGRAMS 6.39%, Arabic Processors 5.75%, NAYEL 4.99%. The organizers' baselines scored 2.13% and 0.03%.
- Rules:
  - Participants received the actual tweet text rather than IDs.
  - External data and user-level metadata such as geolocation were banned.
  - Only the provided 10M unlabeled tweets could be used as extra material.

## Interesting ideas
- **User-disjoint splits.** No author appears in more than one of TRAIN, DEV and TEST, so models cannot score well by memorizing an individual's writing style. The same safeguard would help any author-level or region-level classifier built on social media text, for example Ukrainian regional variation.
- **Sharing raw text instead of IDs.** Every team works on exactly the same data, unaffected by deleted posts. Leaving out IDs also makes it harder to look up an author's profile and leak location information.
- **A large unlabeled companion pool.** Pairing a modest labeled set with millions of unlabeled in-domain posts let teams try further pre-training and self-training, which the strongest systems did. This is a cheap way to push a small benchmark toward semi-supervised methods.
- **Macro-F1 as the headline metric on skewed labels.** With macro-F1, small regions count as much as dominant ones. The large gap between accuracy and macro-F1 in the results shows how misleading accuracy alone would be.
- **Coarse and fine label levels on the same data.** Running country and province tasks side by side shows clearly how fast performance drops as geographic granularity increases. This design could be reused for nested geographic or administrative label sets in other languages.

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

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## Cite

Cite as: Muhammad Abdul-Mageed, Chiyu Zhang, Houda Bouamor and Nizar Habash. 2020. NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task. Proceedings of the Fifth Arabic Natural Language Processing Workshop.

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

```bibtex
@inproceedings{abdulmageed2020nadi,
  title = {{NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task}},
  author = {Muhammad Abdul-Mageed, Chiyu Zhang, Houda Bouamor and Nizar Habash},
  booktitle = {Proceedings of the Fifth Arabic Natural Language Processing Workshop},
  year = {2020},
  url = {https://aclanthology.org/2020.wanlp-1.9/}
}
```

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## References

46 references, 12 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*