---
title: "Capturing Intra-Dialectal Variation in Qatari Arabic: A Corpus of"
description: "The authors build what they describe as the first publicly available, multimodal Qatari corpus. 83.8% accuracy. 33 references."
authors: ["Houda Bouamor", "Sara Al-Emadi", "Zeinab Ibrahim", "Hany Fazzaa", "Aisha Al-Sultan"]
year: 2025
venue: "Proceedings of The Third Arabic Natural Language Processing Conference"
source: "https://aclanthology.org/2025.arabicnlp-main.18/"
doi: "10.18653/v1/2025.arabicnlp-main.18"
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-2025-capturing-intra-dialectal-variation-qatari/"
---
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# Capturing Intra-Dialectal Variation in Qatari Arabic: A Corpus of Cultural and Gender Dimensions

**Authors:** [Houda Bouamor](https://md.research.elamur.ai/free/en/authors/houda-bouamor/), [Sara Al-Emadi](https://md.research.elamur.ai/free/en/authors/sara-al-emadi/), Zeinab Ibrahim, Hany Fazzaa and Aisha Al-Sultan · **Year:** 2025 · **Venue:** [Proceedings of The Third Arabic Natural Language Processing Conference](https://md.research.elamur.ai/free/en/venues/arabicnlp/) ([ArabicNLP 2025](https://md.research.elamur.ai/free/en/venues/arabicnlp/2025/)) · **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/2025.arabicnlp-main.18/) · [PDF](https://aclanthology.org/2025.arabicnlp-main.18.pdf) · [DOI 10.18653/v1/2025.arabicnlp-main.18](https://doi.org/10.18653/v1/2025.arabicnlp-main.18) · [OpenAlex](https://openalex.org/W4416037668)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Qatari Arabic is usually treated as just part of "Gulf Arabic." This hides real differences inside the dialect, especially between Urban and Bedouin speakers. The authors build what they describe as the first publicly available, multimodal Qatari corpus. It links audio recordings to transcripts collected from 255 people who speak the dialect natively, with equal representation across gender, age and sociocultural group.

The data come from direct elicitation. Trained, gender-balanced Qatari interviewers used a questionnaire with five open-ended prompts on culturally important themes, in two parallel versions (one per group). The authors analyse lexical, phonological and morphosyntactic differences, such as lexical diversity and code-switching, and run two baseline text classification tasks.

- **Dialect identification:** a linear SVM on combined word and character TF-IDF features reaches 83.8% accuracy. This beats both logistic regression and fine-tuned AraBERT.
- **Gender prediction:** a simple TF-IDF logistic regression gets 77%, ahead of AraBERT.

The resource supports sociolinguistic study, dialect-aware NLP, and documentation of Qatari linguistic heritage.

## Key points
- **Corpus design:** 255 speakers, stratified by gender, age and Urban vs. Bedouin affiliation (self-identified). The five themes are traditions, social perceptions such as women's work and travel, heritage, national identity (including the FIFA World Cup 2022), and youth interests.
- **Dialect ID with AraBERT** (bert-base-arabertv02):
  - 71.7% accuracy, macro-F1 0.65.
  - Bedouin class: F1 0.80, recall 0.89.
  - Urban class: F1 about 0.51, recall about 0.41.
- **Dialect ID with feature models:** word TF-IDF (1–2 grams) plus character TF-IDF (3–5 grams) with a linear SVM gives 83.8%. The logistic regression baseline gives 77%.
- **Gender prediction:**
  - Setup: an 80/20 split, stratified by dialect and age, with about 83.6k test segments.
  - Logistic regression: 77% accuracy, female F1 0.79, male F1 0.74.
  - AraBERT: 72% accuracy, female F1 0.76, male F1 0.68.
- **Suggested gender markers:** morphophonological suffixes (-ts vs. -š), word preferences and formulaic phrases appear to carry gender signal.

## Interesting ideas
- The elicitation questionnaire comes in two parallel versions with the same topics and structure, so any group differences come from language rather than content.
- A linear model over word bigrams plus character 3–5 grams beat a fine-tuned transformer, because character n-grams pick up morphological variation cheaply.
- Per-class recall showed where the models break: the class with more borrowing and lexical diversity (Urban) was much harder. Reporting by subgroup exposes this, while overall accuracy hides it.
- The test split is stratified by variables other than the label (dialect and age, when predicting gender), which keeps demographic confounds balanced.

## For Mova

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

- **I1** (learn): Build evaluation sets where the same content appears in different English registers or dialects, so drops in parser or frame accuracy can be traced to variety alone.
- **I2** (tokens): Character n-gram features could help the tagger and lemmatizer with nonstandard spellings and dialect morphology without a heavy model.
- **I3** (learn): Report scores per genre, source or dialect subgroup, and treat low recall on a heterogeneous subgroup as a signal to target repair rules there.
- **I4** (learn): When splitting treebanks or silver data, stratify by source and genre so held-out results are not inflated by distribution leakage.

*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 University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) — University, Pittsburgh, United States · 598 works · 191,226 citations
- [Hamad bin Khalifa University](https://md.research.elamur.ai/free/en/universities/hamad-bin-khalifa-university/) — University, Doha, Qatar · 144 works · 14,175 citations
- [Carnegie Mellon University Qatar](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university-qatar/) — University, Doha, Qatar · 48 works · 2,773 citations

Also on the byline: [Georgetown University in Qatar](https://ror.org/029e47x73).

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🧭 Topics (1): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 3,066 works

## Cite

Cite as: Houda Bouamor, Sara Al-Emadi, Zeinab Ibrahim, Hany Fazzaa and Aisha Al-Sultan. 2025. Capturing Intra-Dialectal Variation in Qatari Arabic: A Corpus of Cultural and Gender Dimensions. Proceedings of The Third Arabic Natural Language Processing Conference.

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

```bibtex
@inproceedings{bouamor2025capturing,
  title = {{Capturing Intra-Dialectal Variation in Qatari Arabic: A Corpus of Cultural and Gender Dimensions}},
  author = {Houda Bouamor, Sara Al-Emadi, Zeinab Ibrahim, Hany Fazzaa and Aisha Al-Sultan},
  booktitle = {Proceedings of The Third Arabic Natural Language Processing Conference},
  year = {2025},
  doi = {10.18653/v1/2025.arabicnlp-main.18},
  url = {https://doi.org/10.18653/v1/2025.arabicnlp-main.18}
}
```

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*Updated 2 November 2026*