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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

An example from en config:

{'id': '56beb4343aeaaa14008c925b',
 'context': "The Panthers defense gave up just 308 points, ranking sixth in the league, while also leading the NFL in interceptions with 24 and boasting four Pro Bowl selections. Pro Bowl defensive tackle Kawann Short led the team in sacks with 11, while also forcing three fumbles and recovering two. Fellow lineman Mario Addison added 6½ sacks. The Panthers line also featured veteran defensive end Jared Allen, a 5-time pro bowler who was the NFL's active career sack leader with 136, along with defensive end Kony Ealy, who had 5 sacks in just 9 starts. Behind them, two of the Panthers three starting linebackers were also selected to play in the Pro Bowl: Thomas Davis and Luke Kuechly. Davis compiled 5½ sacks, four forced fumbles, and four interceptions, while Kuechly led the team in tackles (118) forced two fumbles, and intercepted four passes of his own. Carolina's secondary featured Pro Bowl safety Kurt Coleman, who led the team with a career high seven interceptions, while also racking up 88 tackles and Pro Bowl cornerback Josh Norman, who developed into a shutdown corner during the season and had four interceptions, two of which were returned for touchdowns.",
 'question': 'How many points did the Panthers defense surrender?',
 'answers': {'text': ['308'], 'answer_start': [34]}}

Data Fields

• id ( str ): Unique ID for the context-question pair.
• context ( str ): Context for the question.
• question ( str ): Question.
• answers ( dict ): Answers with the following keys:
  • text ( list of str ): Texts of the answers.
  • answer_start ( list of int ): Start positions for every answer text.

Data Splits

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

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.