README.md

metadata

annotations_creators:
  - no-annotation
language:
  - en
language_creators:
  - machine-generated
license:
  - odc-by
multilinguality:
  - monolingual
pretty_name: C4-FAQs
size_categories:
  - 100K<n<1M
source_datasets:
  - extended|c4
tags:
  - question-generation
  - question_generation
  - open-domain-qg
  - qg
task_categories:
  - text2text-generation
  - text-generation
  - question-answering
task_ids:
  - text-simplification
  - language-modeling
  - open-domain-qa

Dataset Card for [Dataset Name]

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• 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

• Point of Contact: Vishal Burman

Dataset Summary

This dataset comprises of open-domain question-answer pairs obtained from extracting 150K FAQ URLs from C4 dataset. Please refer to the original paper and dataset card for more details.

You can load C4-FAQs as follows:

from datasets import load_dataset
c4_faqs_dataset = load_dataset("vishal-burman/c4-faqs")

Supported Tasks and Leaderboards

C4-FAQs is mainly intended for open-domain end-to-end question generation. It can also be used for open-domain question answering.

Languages

C4-FAQs only supports English language.

Dataset Structure

Data Instances

An example of a single dataset point:

{'url': 'https://www.brusselsghosts.com/things-to-do-brussels/faq.html', 'faq_pairs': [{'question': 'What should I bring for the tour?', 'answer': 'Nothing special, just be ready to walk for bit and potentially something to protect you from poltergeists and rain. Any kind of amulet or protection stone is also welcome.'}, {'question': 'Can kids join too ?', 'answer': 'Yes, we accept kids from 6 years old and on! We also have a family discount, if you book for 2 adults and 2 kids!'}, {'question': 'Where is the meeting point ?', 'answer': 'Brussels has many paved roads and those are hardly accessible with a wheelchair, for that reason we have to unfortunately label our tour as not wheelchair accessible.'}]}

Data Fields

The data have several fields:

• url: URL of the webpage containing the FAQs
• faq_pairs: A list of question-answer pairs extracted from the webpage
  • question: A single question as a string
  • answer: A single answer to the above question as a string

Data Splits

subset	total
train	150K

Dataset Creation

Curation Rationale

The dataset was curated to create end-to-end Question Generation pipelines. A large amount of open-source models use SQuAD dataset to create answer-agnostic question generation models. While the questions are valid, they often are short factoid in nature. This dataset is curated from FAQs of websites, which are generally hand-crafted and can be used to further improve generated question quality.

Additional Information

Dataset Curators

Original data by Common Crawl.

Licensing Information

The original dataset was released under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.

Citation Information

If you use this dataset, I would love to hear about it! Reach out on GitHub, twitter or shoot me an email.

To cite the original c4 dataset:

@article{2019t5,
    author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
    title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
    journal = {arXiv e-prints},
    year = {2019},
    archivePrefix = {arXiv},
    eprint = {1910.10683},
}