README.md

annotations_creators	crowdsourced
language	en
language_creators	found
license	cc-by-sa-4.0
multilinguality	monolingual
pretty_name	HotpotQA
size_categories	100K<n<1M
source_datasets	original
task_categories	question-answering
task_ids	question-answering-other-multi-hop
paperswithcode_id	hotpotqa

Dataset Card for "hotpot_qa"

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

• Homepage: https://hotpotqa.github.io/
• Repository: https://github.com/hotpotqa/hotpot
• Paper: HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 1213.88 MB
• Size of the generated dataset: 1186.81 MB
• Total amount of disk used: 2400.69 MB

Dataset Summary

HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems’ ability to extract relevant facts and perform necessary comparison.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

distractor

• Size of downloaded dataset files: 584.36 MB
• Size of the generated dataset: 570.93 MB
• Total amount of disk used: 1155.29 MB

An example of 'validation' looks as follows.

{
    "answer": "This is the answer",
    "context": {
        "sentences": [["Sent 1"], ["Sent 21", "Sent 22"]],
        "title": ["Title1", "Title 2"]
    },
    "id": "000001",
    "level": "medium",
    "question": "What is the answer?",
    "supporting_facts": {
        "sent_id": [0, 1, 3],
        "title": ["Title of para 1", "Title of para 2", "Title of para 3"]
    },
    "type": "comparison"
}

fullwiki

• Size of downloaded dataset files: 629.52 MB
• Size of the generated dataset: 615.88 MB
• Total amount of disk used: 1245.40 MB

An example of 'train' looks as follows.

{
    "answer": "This is the answer",
    "context": {
        "sentences": [["Sent 1"], ["Sent 2"]],
        "title": ["Title1", "Title 2"]
    },
    "id": "000001",
    "level": "hard",
    "question": "What is the answer?",
    "supporting_facts": {
        "sent_id": [0, 1, 3],
        "title": ["Title of para 1", "Title of para 2", "Title of para 3"]
    },
    "type": "bridge"
}

Data Fields

The data fields are the same among all splits.

distractor

• id: a string feature.
• question: a string feature.
• answer: a string feature.
• type: a string feature.
• level: a string feature.
• supporting_facts: a dictionary feature containing:
  • title: a string feature.
  • sent_id: a int32 feature.
• context: a dictionary feature containing:
  • title: a string feature.
  • sentences: a list of string features.

fullwiki

• id: a string feature.
• question: a string feature.
• answer: a string feature.
• type: a string feature.
• level: a string feature.
• supporting_facts: a dictionary feature containing:
  • title: a string feature.
  • sent_id: a int32 feature.
• context: a dictionary feature containing:
  • title: a string feature.
  • sentences: a list of string features.

Data Splits

distractor

	train	validation
distractor	90447	7405

fullwiki

	train	validation	test
fullwiki	90447	7405	7405

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

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

HotpotQA is distributed under a CC BY-SA 4.0 License.

Citation Information

@inproceedings{yang2018hotpotqa,
  title={{HotpotQA}: A Dataset for Diverse, Explainable Multi-hop Question Answering},
  author={Yang, Zhilin and Qi, Peng and Zhang, Saizheng and Bengio, Yoshua and Cohen, William W. and Salakhutdinov, Ruslan and Manning, Christopher D.},
  booktitle={Conference on Empirical Methods in Natural Language Processing ({EMNLP})},
  year={2018}
}

Contributions

Thanks to @albertvillanova, @ghomasHudson for adding this dataset.