---
title: "The MADAR Shared Task on Arabic Fine-Grained — Bouamor 2019"
description: "This report describes a WANLP 2019 shared task that pushed Arabic dialect identification beyond the usual handful of broad regional classes toward much…"
authors: ["Houda Bouamor", "Sabit Hassan", "Nizar Habash"]
year: 2019
venue: "Proceedings of the Fourth Arabic Natural Language Processing Workshop"
source: "https://aclanthology.org/W19-4622/"
doi: "10.18653/v1/w19-4622"
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/bouamor-2019-madar-shared-task-arabic-fine/"
---
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# The MADAR Shared Task on Arabic Fine-Grained Dialect Identification

**Authors:** [Houda Bouamor](https://md.research.elamur.ai/free/en/authors/houda-bouamor/), [Sabit Hassan](https://md.research.elamur.ai/free/en/authors/sabit-hassan/), [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/) · **Year:** 2019 · **Venue:** Proceedings of the Fourth Arabic Natural Language Processing Workshop · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/W19-4622/) · [PDF](https://aclanthology.org/W19-4622.pdf) · [DOI 10.18653/v1/w19-4622](https://doi.org/10.18653/v1/w19-4622) · [OpenAlex](https://openalex.org/W2970513828)

**Citations:** 114 citations (openalex-snapshot · 2026-10-14) · [104](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
This report describes a WANLP 2019 shared task that pushed Arabic dialect identification beyond the usual handful of broad regional classes toward much finer distinctions. It had two subtasks. Subtask 1 labels individual sentences with one of 25 Arab cities or MSA, using the parallel MADAR travel-domain corpus derived from BTEC. Subtask 2 assigns Twitter users to one of 21 Arab countries based only on their tweets, using a new resource, the MADAR Twitter Corpus. For this corpus, the organizers gathered profiles through country-related hashtags and had native-speaker annotators label each one manually. Systems were ranked by macro-averaged F1 on blind test sets, with accuracy also reported at coarser geographic levels. Twenty-one teams from 15 countries took part. In the city-level subtask, no team surpassed the earlier Salameh et al. (2018) system. In the user-level subtask, UBC-NLP won clearly. The task demonstrates strong community interest in fine-grained Arabic dialect modeling, and its datasets were promised for public release.

## Key points
- **Participation:** 21 teams from 15 countries on four continents submitted runs, and 17 system description papers were published.
- **Subtask 1 data:** The MADAR corpus has two parts.
  - Corpus 26 contains 2,000 BTEC sentences translated in parallel into 25 city dialects plus MSA.
  - Corpus 6 adds 10,000 further sentences for only Beirut, Cairo, Doha, Tunis and Rabat, plus MSA.
- **Subtask 1 results:** The top macro F1 was 67.32 (ArbDialectID), followed closely by SMarT at 67.31. This fell just short of the Salameh et al. (2018) reference of 67.89. A character 5-gram LM baseline reached 64.74.
- **MADAR Twitter Corpus:** It contains 2,980 labeled user profiles, with the first 100 tweets kept per user.
  - Annotation agreement on a shared set of 150 profiles reached an average Cohen Kappa of 80.16%.
  - The class distribution is heavily skewed: Saudi Arabia accounts for about 36% of profiles, and Djibouti has only 2.
- **Subtask 2 results:** UBC-NLP won with 71.70 macro F1, about 2 points ahead of Mawdoo3 LTD (69.86). The majority-class baseline (always predicting KSA) scored only 2.64 F1.

## Interesting ideas
- **Neutral hashtag sampling:** Instead of searching for dialect-specific keywords, the organizers collected users through neutral country and pan-Arab hashtags, taking an equal quota per hashtag. This reduces selection bias toward stereotypically dialectal writing, and the same approach could help when building corpora for any language variety.
- **Hierarchical evaluation:** Fine-grained predictions were mapped up to country and region levels to compute additional accuracies. This shows whether a system's errors are "near misses" or completely wrong, which is useful for any classification task with a label hierarchy, such as geography or topic taxonomies.
- **Annotation beyond self-reported location:** Annotators were told not to trust the stated location alone. They could also use extra-linguistic signals in the profile and even linked external profiles. This kind of guideline can make labels more reliable when metadata is noisy or self-declared.
- **Measuring data decay:** The organizers quantified how many tweets and users disappeared over time and checked how this related to team rankings. Reporting such attrition is a good practice for any benchmark built on volatile social media content.
- **Macro F1 under skew:** Macro F1 served as the official metric on a heavily imbalanced dataset, and a majority-class baseline was reported alongside it. This makes clear that a trivial predictor gains almost nothing, which is a simple safeguard against misleading accuracy figures.

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

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

104 papers of our library cite it (of the 140884 whose bibliographies we read); 20 of them have a page here, 84 not yet.

- [Arabic Dialect Identification in the Wild](https://md.research.elamur.ai/free/en/papers/abdelali-2020-arabic-dialect-identification-wild/)
- [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://md.research.elamur.ai/free/en/papers/abdul-2021-mageed-arbert-marbert-deep-bidirectional-transformers/)
- [The SADID Evaluation Datasets for Low-Resource Spoken Language Machine Translation of Arabic Dialects](https://md.research.elamur.ai/free/en/papers/abid-2020-sadid-evaluation-datasets-low-resource/)
- [Parsing Arabic Dialects Revisited: New Benchmarks, Models, and Insights](https://md.research.elamur.ai/free/en/papers/aclbulk-2026-elshabrawy-parsing-arabic-dialects-revisited-new/)
- [AlcLaM: Arabic Dialectal Language Model](https://md.research.elamur.ai/free/en/papers/ahmed-2024-alclam-arabic-dialectal-language-model/)
- [DialectalArabicMMLU: Benchmarking Dialectal Capabilities in Arabic and Multilingual Language Models](https://md.research.elamur.ai/free/en/papers/altakrori-2026-dialectalarabicmmlu-benchmarking-dialectal-capabilities-arabic/)
- [Automatic Arabic Dialect Identification Systems for Written Texts: A Survey](https://md.research.elamur.ai/free/en/papers/althobaiti-2020-automatic-arabic-dialect-identification-systems/)
- [Hierarchical Aggregation of Dialectal Data for Arabic Dialect Identification](https://md.research.elamur.ai/free/en/papers/baimukan-2022-hierarchical-aggregation-dialectal-data-arabic/)
- [An Open Dataset and Model for Language Identification](https://md.research.elamur.ai/free/en/papers/burchell-2023-open-dataset-model-language-identification/)
- [A Spelling Correction Corpus for Multiple Arabic Dialects](https://md.research.elamur.ai/free/en/papers/eryani-2020-spelling-correction-corpus-multiple-arabic/)
- [On the importance of Data Scale in Pretraining Arabic Language Models](https://md.research.elamur.ai/free/en/papers/ghaddar-2024-importance-data-scale-pretraining-arabic/)
- [Habibi - a multi Dialect multi National Arabic Song Lyrics Corpus](https://md.research.elamur.ai/free/en/papers/haj-2020-habibi-multi-dialect-multi-national/)
- [The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/inoue-2021-interplay-variant-size-task-type/)
- [Exploring Data and Parameter Efficient Strategies for Arabic Dialect Identifications](https://md.research.elamur.ai/free/en/papers/kanjirangat-2025-exploring-data-parameter-efficient-strategies/)
- [GlotLID: Language Identification for Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/kargaran-2023-glotlid-language-identification-low-resource/)
- [BERT-based Multi-Task Model for Country and Province Level Modern Standard Arabic and Dialectal Arabic Identification](https://md.research.elamur.ai/free/en/papers/mekki-2021-bert-based-multi-task-model-country/)
- [AraT5: Text-to-Text Transformers for Arabic Language Generation](https://md.research.elamur.ai/free/en/papers/nagoudi-2022-arat5-text-text-transformers-arabic/)
- [Context-Aware Dialectal Arabic Machine Translation with Interactive Region and Register Selection](https://md.research.elamur.ai/free/en/papers/nowshin-2026-context-aware-dialectal-arabic-machine/)
- [A review of sentiment analysis research in Arabic language](https://md.research.elamur.ai/free/en/papers/oueslati-2020-review-sentiment-analysis-research-arabic/)
- [Ramsa: A Large Sociolinguistically Rich Emirati Arabic Speech Corpus for ASR and TTS](https://md.research.elamur.ai/free/en/papers/sabbagh-2026-ramsa-large-sociolinguistically-rich-emirati/)

## Cite

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

```bibtex
@inproceedings{bouamor2019madar,
  title = {{The MADAR Shared Task on Arabic Fine-Grained Dialect Identification}},
  author = {Bouamor, Houda and Hassan, Sabit and Habash, Nizar},
  booktitle = {Proceedings of the Fourth Arabic Natural Language Processing Workshop},
  year = {2019},
  doi = {10.18653/v1/w19-4622},
  url = {https://doi.org/10.18653/v1/w19-4622}
}
```

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