Dataset Card for "natural_questions"

Dataset Summary

The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets.

Supported Tasks

More Information Needed

Languages

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Dataset Structure

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

Data Instances

default

• Size of downloaded dataset files: 42981.34 MB
• Size of the generated dataset: 95175.86 MB
• Total amount of disk used: 138157.19 MB

An example of 'train' looks as follows.

Data Fields

The data fields are the same among all splits.

default

• id: a string feature.
• title: a string feature.
• url: a string feature.
• html: a string feature.
• tokens: a dictionary feature containing:
  • token: a string feature.
  • is_html: a bool feature.
• text: a string feature.
• tokens: a list of string features.
• annotations: a dictionary feature containing:
  • id: a string feature.
  • start_token: a int64 feature.
  • end_token: a int64 feature.
  • start_byte: a int64 feature.
  • end_byte: a int64 feature.
  • short_answers: a dictionary feature containing:
    • start_token: a int64 feature.
    • end_token: a int64 feature.
    • start_byte: a int64 feature.
    • end_byte: a int64 feature.
    • text: a string feature.
  • yes_no_answer: a classification label, with possible values including NO (0), YES (1).

Data Splits Sample Size

Dataset Creation

Curation Rationale

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Source Data

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Annotations

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@article{47761,
title    = {Natural Questions: a Benchmark for Question Answering Research},
author    = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le and Slav Petrov},
year    = {2019},
journal    = {Transactions of the Association of Computational Linguistics}
}

Contributions

Thanks to @thomwolf, @lhoestq for adding this dataset.