README.md

metadata

annotations_creators:
  - expert-generated
  - found
language_creators:
  - expert-generated
  - found
language:
  - en
license:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - 100K<n<1M
  - 10K<n<100K
  - 1K<n<10K
  - 1M<n<10M
source_datasets:
  - extended|other-BioASQ
  - extended|other-DuoRC
  - extended|other-HotpotQA
  - extended|other-Natural-Questions
  - extended|other-Relation-Extraction
  - extended|other-SQuAD
  - extended|other-SearchQA
  - extended|other-TextbookQA
  - extended|other-TriviaQA
task_categories:
  - question-answering
task_ids:
  - extractive-qa
  - open-domain-qa
paperswithcode_id: multireqa
pretty_name: MultiReQA
dataset_info:
  - config_name: SearchQA
    features:
      - name: candidate_id
        dtype: string
      - name: response_start
        dtype: int32
      - name: response_end
        dtype: int32
    splits:
      - name: train
        num_bytes: 183902877
        num_examples: 3163801
      - name: validation
        num_bytes: 26439174
        num_examples: 454836
    download_size: 36991959
    dataset_size: 210342051
  - config_name: TriviaQA
    features:
      - name: candidate_id
        dtype: string
      - name: response_start
        dtype: int32
      - name: response_end
        dtype: int32
    splits:
      - name: train
        num_bytes: 107326326
        num_examples: 1893674
      - name: validation
        num_bytes: 13508062
        num_examples: 238339
    download_size: 21750402
    dataset_size: 120834388
  - config_name: HotpotQA
    features:
      - name: candidate_id
        dtype: string
      - name: response_start
        dtype: int32
      - name: response_end
        dtype: int32
    splits:
      - name: train
        num_bytes: 29516866
        num_examples: 508879
      - name: validation
        num_bytes: 3027229
        num_examples: 52191
    download_size: 6343389
    dataset_size: 32544095
  - config_name: SQuAD
    features:
      - name: candidate_id
        dtype: string
      - name: response_start
        dtype: int32
      - name: response_end
        dtype: int32
    splits:
      - name: train
        num_bytes: 16828974
        num_examples: 95659
      - name: validation
        num_bytes: 2012997
        num_examples: 10642
    download_size: 3003646
    dataset_size: 18841971
  - config_name: NaturalQuestions
    features:
      - name: candidate_id
        dtype: string
      - name: response_start
        dtype: int32
      - name: response_end
        dtype: int32
    splits:
      - name: train
        num_bytes: 28732767
        num_examples: 448355
      - name: validation
        num_bytes: 1418124
        num_examples: 22118
    download_size: 6124487
    dataset_size: 30150891
  - config_name: BioASQ
    features:
      - name: candidate_id
        dtype: string
      - name: response_start
        dtype: int32
      - name: response_end
        dtype: int32
    splits:
      - name: test
        num_bytes: 766190
        num_examples: 14158
    download_size: 156649
    dataset_size: 766190
  - config_name: RelationExtraction
    features:
      - name: candidate_id
        dtype: string
      - name: response_start
        dtype: int32
      - name: response_end
        dtype: int32
    splits:
      - name: test
        num_bytes: 217870
        num_examples: 3301
    download_size: 73019
    dataset_size: 217870
  - config_name: TextbookQA
    features:
      - name: candidate_id
        dtype: string
      - name: response_start
        dtype: int32
      - name: response_end
        dtype: int32
    splits:
      - name: test
        num_bytes: 4182675
        num_examples: 71147
    download_size: 704602
    dataset_size: 4182675
  - config_name: DuoRC
    features:
      - name: candidate_id
        dtype: string
      - name: response_start
        dtype: int32
      - name: response_end
        dtype: int32
    splits:
      - name: test
        num_bytes: 1483518
        num_examples: 5525
    download_size: 97625
    dataset_size: 1483518
config_names:
  - BioASQ
  - DuoRC
  - HotpotQA
  - NaturalQuestions
  - RelationExtraction
  - SQuAD
  - SearchQA
  - TextbookQA
  - TriviaQA

Dataset Card for MultiReQA

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://github.com/google-research-datasets/MultiReQA
• Repository: https://github.com/google-research-datasets/MultiReQA
• Paper: https://arxiv.org/pdf/2005.02507.pdf
• Leaderboard:
• Point of Contact:

Dataset Summary

MultiReQA contains the sentence boundary annotation from eight publicly available QA datasets including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, and TextbookQA. Five of these datasets, including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, contain both training and test data, and three, in cluding BioASQ, RelationExtraction, TextbookQA, contain only the test data (also includes DuoRC but not specified in the official documentation)

Supported Tasks and Leaderboards

• Question answering (QA)
• Retrieval question answering (ReQA)

Languages

Sentence boundary annotation for SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, TextbookQA and DuoRC

Dataset Structure

Data Instances

The general format is: { "candidate_id": <candidate_id>, "response_start": <response_start>, "response_end": <response_end> } ...

An example from SearchQA: {'candidate_id': 'SearchQA_000077f3912049dfb4511db271697bad/_0_1', 'response_end': 306, 'response_start': 243}

Data Fields

{ "candidate_id": <STRING>, "response_start": <INT>, "response_end": <INT> } ...

• candidate_id: The candidate id of the candidate sentence. It consists of the original qid from the MRQA shared task.
• response_start: The start index of the sentence with respect to its original context.
• response_end: The end index of the sentence with respect to its original context

Data Splits

Train and Dev splits are available only for the following datasets,

• SearchQA
• TriviaQA
• HotpotQA
• SQuAD
• NaturalQuestions

Test splits are available only for the following datasets,

• BioASQ
• RelationExtraction
• TextbookQA

The number of candidate sentences for each dataset in the table below.

	MultiReQA
	train	test
SearchQA	629,160	454,836
TriviaQA	335,659	238,339
HotpotQA	104,973	52,191
SQuAD	87,133	10,642
NaturalQuestions	106,521	22,118
BioASQ	-	14,158
RelationExtraction	-	3,301
TextbookQA	-	3,701

Dataset Creation

Curation Rationale

MultiReQA is a new multi-domain ReQA evaluation suite composed of eight retrieval QA tasks drawn from publicly available QA datasets from the MRQA shared task. The dataset was curated by converting existing QA datasets from MRQA shared task to the format of MultiReQA benchmark.

Source Data

Initial Data Collection and Normalization

The Initial data collection was performed by converting existing QA datasets from MRQA shared task to the format of MultiReQA benchmark.

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

The annotators/curators of the dataset are mandyguo-xyguo and mwurts4google, the contributors of the official MultiReQA github repository

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

The annotators/curators of the dataset are mandyguo-xyguo and mwurts4google, the contributors of the official MultiReQA github repository

Licensing Information

[More Information Needed]

Citation Information

@misc{m2020multireqa,
    title={MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models},
    author={Mandy Guo and Yinfei Yang and Daniel Cer and Qinlan Shen and Noah Constant},
    year={2020},
    eprint={2005.02507},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Contributions

Thanks to @Karthik-Bhaskar for adding this dataset.