Datasets:
xquad_r

Task Categories: question-answering
Multilinguality: monolingual
Size Categories: 1K<n<10K
Language Creators: found
Annotations Creators: expert-generated

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Dataset Summary

XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset). Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each question appears in 11 different languages and has 11 parallel correct answers across the languages.

Supported Tasks and Leaderboards

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Languages

The dataset can be found with the following languages:

  • Arabic: xquad-r/ar.json
  • German: xquad-r/de.json
  • Greek: xquad-r/el.json
  • English: xquad-r/en.json
  • Spanish: xquad-r/es.json
  • Hindi: xquad-r/hi.json
  • Russian: xquad-r/ru.json
  • Thai: xquad-r/th.json
  • Turkish: xquad-r/tr.json
  • Vietnamese: xquad-r/vi.json
  • Chinese: xquad-r/zh.json

Dataset Structure

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Data Instances

The number of questions and candidate sentences for each language for XQuAD-R is shown in the table below:

XQuAD-R
questions candidates
ar 1190 1222
de 1190 1276
el 1190 1234
en 1190 1180
es 1190 1215
hi 1190 1244
ru 1190 1219
th 1190 852
tr 1190 1167
vi 1190 1209
zh 1190 1196

Data Fields

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Data Splits

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Dataset Creation

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Curation Rationale

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Source Data

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Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

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Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

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Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

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Dataset Curators

The dataset was initially created by Uma Roy, Noah Constant, Rami Al-Rfou, Aditya Barua, Aaron Phillips and Yinfei Yang, during work done at Google Research.

Licensing Information

XQuAD-R is distributed under the CC BY-SA 4.0 license.

Citation Information

@article{roy2020lareqa,
  title={LAReQA: Language-agnostic answer retrieval from a multilingual pool},
  author={Roy, Uma and Constant, Noah and Al-Rfou, Rami and Barua, Aditya and Phillips, Aaron and Yang, Yinfei},
  journal={arXiv preprint arXiv:2004.05484},
  year={2020}
}

Contributions

Thanks to @manandey for adding this dataset.

Models trained or fine-tuned on xquad_r

None yet