task_categories:question-answering task_ids:extractive-qa annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:multilingual size_categories:10K<n<100K source_datasets:original language:en language:de language:es language:ar language:zh language:vi language:hi license:cc-by-sa-3.0 region:us
    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.
@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}
}
---
pretty_name: MLQA (MultiLingual Question Answering)
language:
- en
- de
- es
- ar
- zh
- vi
- hi
license:
- cc-by-sa-3.0
source_datasets:
- original
size_categories:
- 10K<n<100K
language_creators:
- crowdsourced
annotations_creators:
- crowdsourced
multilinguality:
- multilingual
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: mlqa
dataset_info:
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---

# Dataset Card for "mlqa"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [https://github.com/facebookresearch/MLQA](https://github.com/facebookresearch/MLQA)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 4.15 GB
- **Size of the generated dataset:** 910.01 MB
- **Total amount of disk used:** 5.06 GB

### 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](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Languages

MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese.

## Dataset Structure

### Data Instances

#### mlqa-translate-test.ar

- **Size of downloaded dataset files:** 10.08 MB
- **Size of the generated dataset:** 5.48 MB
- **Total amount of disk used:** 15.56 MB

An example of 'test' looks as follows.
```

```

#### mlqa-translate-test.de

- **Size of downloaded dataset files:** 10.08 MB
- **Size of the generated dataset:** 3.88 MB
- **Total amount of disk used:** 13.96 MB

An example of 'test' looks as follows.
```

```

#### mlqa-translate-test.es

- **Size of downloaded dataset files:** 10.08 MB
- **Size of the generated dataset:** 3.92 MB
- **Total amount of disk used:** 13.99 MB

An example of 'test' looks as follows.
```

```

#### mlqa-translate-test.hi

- **Size of downloaded dataset files:** 10.08 MB
- **Size of the generated dataset:** 4.61 MB
- **Total amount of disk used:** 14.68 MB

An example of 'test' looks as follows.
```

```

#### mlqa-translate-test.vi

- **Size of downloaded dataset files:** 10.08 MB
- **Size of the generated dataset:** 6.00 MB
- **Total amount of disk used:** 16.07 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

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the source language producers?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Annotations

#### Annotation process

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the annotators?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Personal and Sensitive Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Discussion of Biases

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Other Known Limitations

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Additional Information

### Dataset Curators

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Licensing Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 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,
  eid = {arXiv: 1910.07475}
}

```


### Contributions

Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@M-Salti](https://github.com/M-Salti), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq) for adding this dataset.