README.md

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Dataset Card for "arcd"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits Sample Size
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://github.com/husseinmozannar/SOQAL/tree/master/data
• Repository: More Information Needed
• Paper: More Information Needed
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 1.85 MB
• Size of the generated dataset: 1.62 MB
• Total amount of disk used: 3.47 MB

Dataset Summary

Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles.

Supported Tasks

More Information Needed

Languages

More Information Needed

Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

plain_text

• Size of downloaded dataset files: 1.85 MB
• Size of the generated dataset: 1.62 MB
• Total amount of disk used: 3.47 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "answers": "{\"answer_start\": [34], \"text\": [\"صحابي من صحابة رسول الإسلام محمد، وعمُّه وأخوه من الرضاعة وأحد وزرائه الأربعة عشر،\"]}...",
    "context": "\"حمزة بن عبد المطلب الهاشمي القرشي صحابي من صحابة رسول الإسلام محمد، وعمُّه وأخوه من الرضاعة وأحد وزرائه الأربعة عشر، وهو خير أع...",
    "id": "621723207492",
    "question": "من هو حمزة بن عبد المطلب؟",
    "title": "حمزة بن عبد المطلب"
}

Data Fields

The data fields are the same among all splits.

plain_text

• id: a string feature.
• title: a string feature.
• context: a string feature.
• question: a string feature.
• answers: a dictionary feature containing:
  • text: a string feature.
  • answer_start: a int32 feature.

Data Splits Sample Size

name	train	validation
plain_text	693	702

Dataset Creation

Curation Rationale

More Information Needed

Source Data

More Information Needed

Annotations

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

Citation Information

@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.",
}

Contributions

Thanks to @albertvillanova, @lewtun, @mariamabarham, @thomwolf, @tayciryahmed for adding this dataset.