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 and Leaderboards

https://ai.google.com/research/NaturalQuestions

Languages

Dataset Structure

Data Instances

Size of downloaded dataset files: 45.07 GB
Size of the generated dataset: 99.80 GB
Total amount of disk used: 144.87 GB

An example of 'train' looks as follows. This is a toy example.

{
  "id": "797803103760793766",
  "document": {
    "title": "Google",
    "url": "http://www.wikipedia.org/Google",
    "html": "<html><body><h1>Google Inc.</h1><p>Google was founded in 1998 By:<ul><li>Larry</li><li>Sergey</li></ul></p></body></html>",
    "tokens":[
      {"token": "<h1>", "start_byte": 12, "end_byte": 16, "is_html": True},
      {"token": "Google", "start_byte": 16, "end_byte": 22, "is_html": False},
      {"token": "inc", "start_byte": 23, "end_byte": 26, "is_html": False},
      {"token": ".", "start_byte": 26, "end_byte": 27, "is_html": False},
      {"token": "</h1>", "start_byte": 27, "end_byte": 32, "is_html": True},
      {"token": "<p>", "start_byte": 32, "end_byte": 35, "is_html": True},
      {"token": "Google", "start_byte": 35, "end_byte": 41, "is_html": False},
      {"token": "was", "start_byte": 42, "end_byte": 45, "is_html": False},
      {"token": "founded", "start_byte": 46, "end_byte": 53, "is_html": False},
      {"token": "in", "start_byte": 54, "end_byte": 56, "is_html": False},
      {"token": "1998", "start_byte": 57, "end_byte": 61, "is_html": False},
      {"token": "by", "start_byte": 62, "end_byte": 64, "is_html": False},
      {"token": ":", "start_byte": 64, "end_byte": 65, "is_html": False},
      {"token": "<ul>", "start_byte": 65, "end_byte": 69, "is_html": True},
      {"token": "<li>", "start_byte": 69, "end_byte": 73, "is_html": True},
      {"token": "Larry", "start_byte": 73, "end_byte": 78, "is_html": False},
      {"token": "</li>", "start_byte": 78, "end_byte": 83, "is_html": True},
      {"token": "<li>", "start_byte": 83, "end_byte": 87, "is_html": True},
      {"token": "Sergey", "start_byte": 87, "end_byte": 92, "is_html": False},
      {"token": "</li>", "start_byte": 92, "end_byte": 97, "is_html": True},
      {"token": "</ul>", "start_byte": 97, "end_byte": 102, "is_html": True},
      {"token": "</p>", "start_byte": 102, "end_byte": 106, "is_html": True}
    ],
  },
  "question" :{
    "text": "who founded google",
    "tokens": ["who", "founded", "google"]
  },
  "long_answer_candidates": [
    {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "top_level": True},
    {"start_byte": 65, "end_byte": 102, "start_token": 13, "end_token": 21, "top_level": False},
    {"start_byte": 69, "end_byte": 83, "start_token": 14, "end_token": 17, "top_level": False},
    {"start_byte": 83, "end_byte": 92, "start_token": 17, "end_token": 20 , "top_level": False}
  ],
  "annotations": [{
    "id": "6782080525527814293",
    "long_answer": {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "candidate_index": 0},
    "short_answers": [
      {"start_byte": 73, "end_byte": 78, "start_token": 15, "end_token": 16, "text": "Larry"},
      {"start_byte": 87, "end_byte": 92, "start_token": 18, "end_token": 19, "text": "Sergey"}
    ],
    "yes_no_answer": -1
  }]
}

Data Fields

The data fields are the same among all splits.

default

id: a string feature.
document a dictionary feature containing:
- 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.
  - start_byte: a int64 feature.
  - end_byte: a int64 feature.
question: a dictionary feature containing:
- text: a string feature.
- tokens: a list of string features.
long_answer_candidates: a dictionary feature containing:
- start_token: a int64 feature.
- end_token: a int64 feature.
- start_byte: a int64 feature.
- end_byte: a int64 feature.
- top_level: a bool feature.
annotations: a dictionary feature containing:
- id: a string feature.
- long_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.
  - candidate_index: 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

name	train	validation
default	307373	7830
dev	N/A	7830

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

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

Creative Commons Attribution-ShareAlike 3.0 Unported.

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}
}