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---
dataset_info:
  features:
  - name: index
    dtype: string
  - name: question
    dtype: string
  - name: context
    dtype: string
  - name: text
    dtype: string
  - name: answer_start
    dtype: int64
  - name: c_id
    dtype: int64
  splits:
  - name: train
    num_bytes: 61868003
    num_examples: 48344
  download_size: 10512179
  dataset_size: 61868003
---
# Dataset Card for "Arabic_SQuAD"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

---
# Citation
```
@inproceedings{mozannar-etal-2019-neural,
    title = "Neural {A}rabic Question Answering",
    author = "Mozannar, Hussein  and
      Maamary, Elie  and
      El Hajal, Karl  and
      Hajj, Hazem",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4612",
    doi = "10.18653/v1/W19-4612",
    pages = "108--118",
    abstract = "This paper tackles the problem of open domain factual Arabic question answering (QA) using Wikipedia as our knowledge source. This constrains the answer of any question to be a span of text in Wikipedia. Open domain QA for Arabic entails three challenges: annotated QA datasets in Arabic, large scale efficient information retrieval and machine reading comprehension. To deal with the lack of Arabic QA datasets we present the Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles, and a machine translation of the Stanford Question Answering Dataset (Arabic-SQuAD). Our system for open domain question answering in Arabic (SOQAL) is based on two components: (1) a document retriever using a hierarchical TF-IDF approach and (2) a neural reading comprehension model using the pre-trained bi-directional transformer BERT. Our experiments on ARCD indicate the effectiveness of our approach with our BERT-based reader achieving a 61.3 F1 score, and our open domain system SOQAL achieving a 27.6 F1 score.",
}
```
---
