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Dataset Card for "mlqa"

Dataset Summary

MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

mlqa-translate-test.ar

  • Size of downloaded dataset files: 9.61 MB
  • Size of the generated dataset: 5.23 MB
  • Total amount of disk used: 14.84 MB

An example of 'test' looks as follows.


mlqa-translate-test.de

  • Size of downloaded dataset files: 9.61 MB
  • Size of the generated dataset: 3.70 MB
  • Total amount of disk used: 13.31 MB

An example of 'test' looks as follows.


mlqa-translate-test.es

  • Size of downloaded dataset files: 9.61 MB
  • Size of the generated dataset: 3.74 MB
  • Total amount of disk used: 13.34 MB

An example of 'test' looks as follows.


mlqa-translate-test.hi

  • Size of downloaded dataset files: 9.61 MB
  • Size of the generated dataset: 4.40 MB
  • Total amount of disk used: 14.00 MB

An example of 'test' looks as follows.


mlqa-translate-test.vi

  • Size of downloaded dataset files: 9.61 MB
  • Size of the generated dataset: 5.72 MB
  • Total amount of disk used: 15.33 MB

An example of 'test' looks as follows.


Data Fields

The data fields are the same among all splits.

mlqa-translate-test.ar

  • context: a string feature.
  • question: a string feature.
  • answers: a dictionary feature containing:
    • answer_start: a int32 feature.
    • text: a string feature.
  • id: a string feature.

mlqa-translate-test.de

  • context: a string feature.
  • question: a string feature.
  • answers: a dictionary feature containing:
    • answer_start: a int32 feature.
    • text: a string feature.
  • id: a string feature.

mlqa-translate-test.es

  • context: a string feature.
  • question: a string feature.
  • answers: a dictionary feature containing:
    • answer_start: a int32 feature.
    • text: a string feature.
  • id: a string feature.

mlqa-translate-test.hi

  • context: a string feature.
  • question: a string feature.
  • answers: a dictionary feature containing:
    • answer_start: a int32 feature.
    • text: a string feature.
  • id: a string feature.

mlqa-translate-test.vi

  • context: a string feature.
  • question: a string feature.
  • answers: a dictionary feature containing:
    • answer_start: a int32 feature.
    • text: a string feature.
  • id: a string feature.

Data Splits

name test
mlqa-translate-test.ar 5335
mlqa-translate-test.de 4517
mlqa-translate-test.es 5253
mlqa-translate-test.hi 4918
mlqa-translate-test.vi 5495

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

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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{lewis2019mlqa,
  title={MLQA: Evaluating Cross-lingual Extractive Question Answering},
  author={Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger},
  journal={arXiv preprint arXiv:1910.07475},
  year={2019}
}

Contributions

Thanks to @patrickvonplaten, @M-Salti, @lewtun, @thomwolf, @mariamabarham, @lhoestq for adding this dataset.

Models trained or fine-tuned on mlqa