README.md

---
license: other
license_name: creative-commons-attribution-noncommercial-noderivatives-4-0-international
license_link: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
task_categories:
- question-answering
language:
- ar
tags:
- arabic
- cross-dialect
- parallel
- extractive-qa
- squad-format
- msa
- egyptian-arabic
- gulf-arabic
- levantine-arabic
- maghrebi-arabic
- vlogs
- narratives
- curated
- evaluation-benchmark
- cross-lingual-transfer
pretty_name: 'ArDQA: Cross-Dialectal Arabic QA Benchmark'
size_categories:
- 1K<n<10K
---

# Dataset Card for ArDQA

ArDQA is a cross-dialect Arabic QA benchmark spanning three domains. Each domain provides **parallel** QA triples `{context, question, answer}` across **five** Arabic varieties: **MSA, Egyptian, Gulf, Levantine, Maghrebi**. The benchmark contains **8,150** QA triples overall and is designed for evaluation of **cross-dialectal transfer** in Arabic extractive QA.

## Dataset Details

### Dataset Description
- **Curated by:** Native-speaker annotators (see Annotation section).
- **Funded by [optional]:** N/A.
- **Language(s) (NLP):** Arabic (MSA, dialects: Egyptian, Gulf, Levantine, Maghrebi).
- **License:** **CC BY-NC-ND 4.0**  
  Research/teaching use, attribution required, **no commercial use**, **no derivatives**.  
  Legal text: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode

#### Composition
- **ArDQA-SQuAD**: Curated from Arabic-SQuAD v2.0, then translated by native speakers into four dialects with manual span annotation to preserve one-to-one alignment.
- **ArDQA-Vlogs**: Colloquial lifestyle vlog transcripts --> QA construction --> dialect translations --> manual span annotation.
- **ArDQA-Narratives**: Cultural narratives and folklore from online videos, following the same pipeline as Vlogs, with longer, descriptive answers.

#### Quality control
Native speakers translated independently in every domain, cross-checked each other, and an expert adjudicated disagreements. Span consistency was validated (using answer-to-context length ratios) to maintain strict alignment across dialects.

### Paper
Under Review

### Direct Use
- Evaluation of **zero-shot** and **few-shot** cross-dialectal transfer in Arabic QA.
- Analysis of dialectal robustness for Arabic extractive QA models.
- Benchmarking domain sensitivity across SQuAD-like, vlog, and narrative content.

## Dataset Structure

### Format
ArDQA follows **SQuAD v2.0 JSON**:

```text
root
├── data: [
│   ├── {
│   │   ├── title: string
│   │   └── paragraphs: [
│   │       ├── {
│   │       │   ├── context: string
│   │       │   └── qas: [
│   │       │       ├── {
│   │       │       │   id: string
│   │       │       │   question: string
│   │       │       │   is_impossible: boolean
│   │       │       │   answers: [
│   │       │       │       ├── { text: string, answer_start: int }
│   │       │       │       └── ...
│   │       │       └── ...
│   │       └── ...
│   └── ...
└── (optional) version: string
```

### Splits

Each ArDQA domain is divided into **development** and **test** splits to enable zero-shot evaluation (train on MSA or other sources, then evaluate on dialects without target-dialect fine-tuning).

**Counts per domain and split**

| fold | ArDQA-SQuAD (# parallel / # total) | ArDQA-Vlogs (# parallel / # total) | ArDQA-Narratives (# parallel / # total) |
|---|---:|---:|---:|
| dev  | 131 / 655  | 171 / 855  | 160 / 800  |
| test | 368 / 1,840 | 436 / 2,180 | 364 / 1,820 |

- **# parallel** = Number of {context, question, answer} triples aligned across all five Arabic varieties.  
- **# total** = # parallel × 5 dialects (MSA, Egyptian, Gulf, Levantine, Maghrebi).  
- Totals across all domains: **dev = 2,310**, **test = 5,840**, **overall = 8,150** QA triples.



### Source Data
Original texts come from Arabic-SQuAD v2.0 and public online video transcripts (vlogs, narratives). QA items and dialect translations were produced by native-speaker annotators.

### Annotations

#### Annotation process
- Native speakers independently translate and annotate spans.
- Cross-review and expert adjudication.
- Consistency checks (e.g., answer length vs. context, span alignment across dialects).

## Experiment (brief)
We evaluate **zero-shot cross-dialectal transfer** by training only on **MSA** (Arabic-SQuAD v2.0) and testing **zero-shot** on dialectal data.
- **Models:** AraELECTRA-MSA-QA, CAMeLBERT-MSA-QA, AraBERT-MSA-QA.
- **Data:** ArDQA dev/test across **three domains** (SQuAD, Vlogs, Narratives) and **five varieties**: MSA, Egyptian (EGY), Gulf (GLF), Levantine (LEV), Maghrebi (MGR).

#### Model References
- AraELECTRA-MSA-QA. Hugging Face model card. https://huggingface.co/ZeyadAhmed/AraElectra-Arabic-SQuADv2-QA
- CAMeLBERT-MSA (bert-base-arabic-camelbert-msa). Hugging Face model card. https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa
- AraBERT-MSA-QA (bert-large-arabertv02). Hugging Face model card. https://huggingface.co/aubmindlab/bert-large-arabertv02

## Evaluation Metrics
- **EM (Exact Match):** 1 if the predicted span matches the gold answer exactly; else 0.
- **F1:** token-level harmonic mean of precision and recall between predicted and gold spans (rewards partial overlap).

## Reference Results (Zero-Shot Cross-Dialectal Transfer)

**ArDQA-SQuAD (F1 / EM)**  
| Model | EGY | GLF | LEV | MGR | MSA |
|---|---|---|---|---|---|
| AraELECTRA-MSA-QA | 71.66 / 59.51 | 73.76 / 60.87 | 66.72 / 50.54 | 66.35 / 53.80 | 76.19 / 61.96 |
| CAMeLBERT-MSA-QA  | 53.98 / 28.04 | 54.91 / 26.68 | 51.49 / 25.86 | 46.90 / 23.96 | 60.27 / 29.13 |
| AraBERT-MSA-QA    | 12.53 / 4.04  | 11.01 / 3.74  | 12.01 / 3.88  | 12.06 / 3.54  | 11.80 / 3.74  |

**ArDQA-Vlogs (F1 / EM)**  
| Model | EGY | GLF | LEV | MGR | MSA |
|---|---|---|---|---|---|
| AraELECTRA-MSA-QA | 63.90 / 37.93 | 64.47 / 41.74 | 63.00 / 40.37 | 57.11 / 31.19 | 67.01 / 42.20 |
| CAMeLBERT-MSA-QA  | 40.66 / 15.09 | 39.12 / 14.63 | 37.50 / 14.17 | 29.69 / 10.04 | 46.66 / 16.01 |
| AraBERT-MSA-QA    | 13.08 / 4.03  | 11.18 / 4.03  | 12.03 / 4.45  | 12.49 / 4.03  | 11.53 / 4.68  |

**ArDQA-Narratives (F1 / EM)**  
| Model | EGY | GLF | LEV | MGR | MSA |
|---|---|---|---|---|---|
| AraELECTRA-MSA-QA | 35.75 / 11.26 | 40.80 / 14.20 | 38.31 / 12.98 | 31.70 / 6.87 | 43.83 / 14.05 |
| CAMeLBERT-MSA-QA  | 22.33 / 5.82  | 25.53 / 8.02  | 20.74 / 6.56  | 23.82 / 7.47 | 25.13 / 9.68  |
| AraBERT-MSA-QA    | 16.20 / 4.01  | 16.82 / 4.27  | 15.41 / 4.01  | 18.72 / 4.27 | 18.07 / 4.01  |


## Citation

If you use ArDQA, please cite **the dataset**

### Dataset

**BibTeX**
```bibtex
@dataset{ardqa_dataset_2025,
  title   = {ArDQA: Cross-Dialect Arabic QA Benchmark},
  authot =  {Althobaiti, Maha Jarallah}
  year    = {2025},
  note    = {Hugging Face dataset, CC BY-NC-ND 4.0},
  url     = {https://huggingface.co/datasets/MahaJar/ArDQA}
}