README.md

metadata

annotations_creators:
  - found
language:
  - en
language_creators:
  - found
license:
  - unknown
multilinguality:
  - monolingual
pretty_name: SearchQA
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - question-answering
task_ids:
  - extractive-qa
paperswithcode_id: searchqa
dataset_info:
  - config_name: raw_jeopardy
    features:
      - name: category
        dtype: string
      - name: air_date
        dtype: string
      - name: question
        dtype: string
      - name: value
        dtype: string
      - name: answer
        dtype: string
      - name: round
        dtype: string
      - name: show_number
        dtype: int32
      - name: search_results
        sequence:
          - name: urls
            dtype: string
          - name: snippets
            dtype: string
          - name: titles
            dtype: string
          - name: related_links
            dtype: string
    splits:
      - name: train
        num_bytes: 7770972348
        num_examples: 216757
    download_size: 3314386157
    dataset_size: 7770972348
  - config_name: train_test_val
    features:
      - name: category
        dtype: string
      - name: air_date
        dtype: string
      - name: question
        dtype: string
      - name: value
        dtype: string
      - name: answer
        dtype: string
      - name: round
        dtype: string
      - name: show_number
        dtype: int32
      - name: search_results
        sequence:
          - name: urls
            dtype: string
          - name: snippets
            dtype: string
          - name: titles
            dtype: string
          - name: related_links
            dtype: string
    splits:
      - name: train
        num_bytes: 5303005740
        num_examples: 151295
      - name: test
        num_bytes: 1466749978
        num_examples: 43228
      - name: validation
        num_bytes: 740962715
        num_examples: 21613
    download_size: 3148550732
    dataset_size: 7510718433

Dataset Card for "search_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

• Repository: https://github.com/nyu-dl/dl4ir-searchQA
• Paper: SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 6.46 GB
• Size of the generated dataset: 15.28 GB
• Total amount of disk used: 21.74 GB

Dataset Summary

We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering. Unlike recently released datasets, such as DeepMind CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to reflect a full pipeline of general question-answering. That is, we start not from an existing article and generate a question-answer pair, but start from an existing question-answer pair, crawled from J! Archive, and augment it with text snippets retrieved by Google. Following this approach, we built SearchQA, which consists of more than 140k question-answer pairs with each pair having 49.6 snippets on average. Each question-answer-context tuple of the SearchQA comes with additional meta-data such as the snippet's URL, which we believe will be valuable resources for future research. We conduct human evaluation as well as test two baseline methods, one simple word selection and the other deep learning based, on the SearchQA. We show that there is a meaningful gap between the human and machine performances. This suggests that the proposed dataset could well serve as a benchmark for question-answering.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

raw_jeopardy

• Size of downloaded dataset files: 3.31 GB
• Size of the generated dataset: 7.77 GB
• Total amount of disk used: 11.09 GB

An example of 'train' looks as follows.

train_test_val

• Size of downloaded dataset files: 3.15 GB
• Size of the generated dataset: 7.51 GB
• Total amount of disk used: 10.66 GB

An example of 'validation' looks as follows.

Data Fields

The data fields are the same among all splits.

raw_jeopardy

• category: a string feature.
• air_date: a string feature.
• question: a string feature.
• value: a string feature.
• answer: a string feature.
• round: a string feature.
• show_number: a int32 feature.
• search_results: a dictionary feature containing:
  • urls: a string feature.
  • snippets: a string feature.
  • titles: a string feature.
  • related_links: a string feature.

train_test_val

• category: a string feature.
• air_date: a string feature.
• question: a string feature.
• value: a string feature.
• answer: a string feature.
• round: a string feature.
• show_number: a int32 feature.
• search_results: a dictionary feature containing:
  • urls: a string feature.
  • snippets: a string feature.
  • titles: a string feature.
  • related_links: a string feature.

Data Splits

raw_jeopardy

	train
raw_jeopardy	216757

train_test_val

	train	validation	test
train_test_val	151295	21613	43228

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

More Information Needed

Citation Information

@article{DBLP:journals/corr/DunnSHGCC17,
    author    = {Matthew Dunn and
                Levent Sagun and
                Mike Higgins and
                V. Ugur G{"{u}}ney and
                Volkan Cirik and
                Kyunghyun Cho},
    title     = {SearchQA: {A} New Q{\&}A Dataset Augmented with Context from a
                Search Engine},
    journal   = {CoRR},
    volume    = {abs/1704.05179},
    year      = {2017},
    url       = {http://arxiv.org/abs/1704.05179},
    archivePrefix = {arXiv},
    eprint    = {1704.05179},
    timestamp = {Mon, 13 Aug 2018 16:47:09 +0200},
    biburl    = {https://dblp.org/rec/journals/corr/DunnSHGCC17.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

Contributions

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