
# Dataset Card for MKQA: Multilingual Knowledge Questions & Answers

## 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/apple/ml-mkqa/)
- [**Paper:**](https://arxiv.org/abs/2007.15207)

### Dataset Summary

MKQA contains 10,000 queries sampled from the [Google Natural Questions dataset](https://github.com/google-research-datasets/natural-questions).  

For each query we collect new passage-independent answers. 
These queries and answers are then human translated into 25 Non-English languages.

### Supported Tasks and Leaderboards

`question-answering`

### Languages

| Language code | Language name |
|---------------|---------------|
| `ar`     | `Arabic`                    |
| `da`     | `Danish`                    |
| `de`     | `German`                    |
| `en`     | `English`                   |
| `es`     | `Spanish`                   |
| `fi`     | `Finnish`                   |
| `fr`     | `French`                    |
| `he`     | `Hebrew`                    |
| `hu`     | `Hungarian`                 |
| `it`     | `Italian`                   |
| `ja`     | `Japanese`                  |
| `ko`     | `Korean`                    |
| `km`     | `Khmer`                    |
| `ms`     | `Malay`                     |
| `nl`     | `Dutch`                     |
| `no`     | `Norwegian`                 |
| `pl`     | `Polish`                    |
| `pt`     | `Portuguese`                |
| `ru`     | `Russian`                   |
| `sv`     | `Swedish`                   |
| `th`     | `Thai`                      |
| `tr`     | `Turkish`                   |
| `vi`     | `Vietnamese`                |
| `zh_cn`     | `Chinese (Simplified)`   |
| `zh_hk`     | `Chinese (Hong kong)`    |
| `zh_tw`     | `Chinese (Traditional)`  |

## Dataset Structure

### Data Instances

An example from the data set looks as follows:

```
{
 'example_id': 563260143484355911,
 'queries': {
  'en': "who sings i hear you knocking but you can't come in",
  'ru': "кто поет i hear you knocking but you can't come in",
  'ja': '「 I hear you knocking」は誰が歌っていますか',
  'zh_cn': "《i hear you knocking but you can't come in》是谁演唱的",
  ...
 },
 'query': "who sings i hear you knocking but you can't come in",
 'answers': {'en': [{'type': 'entity',
    'entity': 'Q545186',
    'text': 'Dave Edmunds',
    'aliases': []}],
  'ru': [{'type': 'entity',
    'entity': 'Q545186',
    'text': 'Эдмундс, Дэйв',
    'aliases': ['Эдмундс', 'Дэйв Эдмундс', 'Эдмундс Дэйв', 'Dave Edmunds']}],
  'ja': [{'type': 'entity',
    'entity': 'Q545186',
    'text': 'デイヴ・エドモンズ',
    'aliases': ['デーブ・エドモンズ', 'デイブ・エドモンズ']}],
  'zh_cn': [{'type': 'entity', 'text': '戴维·埃德蒙兹 ', 'entity': 'Q545186'}],
  ...
  },
}

```

### Data Fields

Each example in the dataset contains the unique Natural Questions `example_id`, the original English `query`, and then `queries` and `answers` in 26 languages.
Each answer is labelled with an answer type. The breakdown is:

| Answer Type | Occurrence |
|---------------|---------------|
| `entity`               | `4221`             |
| `long_answer`          | `1815`             |
| `unanswerable`         | `1427`             |
| `date`                 | `1174`             |
| `number`               | `485`              |
| `number_with_unit`     | `394`              |
| `short_phrase`         | `346`              |
| `binary`               | `138`              |
  
For each language, there can be more than one acceptable textual answer, in order to capture a variety of possible valid answers. 

Detailed explanation of fields taken from [here](https://github.com/apple/ml-mkqa/#dataset)

when `entity` field is not available it is set to an empty string ''.
when `aliases` field is not available it is set to an empty list [].

### Data Splits

- Train: 10000

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

[Google Natural Questions dataset](https://github.com/google-research-datasets/natural-questions)

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### 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

[More Information Needed]

### Licensing Information

[CC BY-SA 3.0](https://github.com/apple/ml-mkqa#license)

### Citation Information
```
@misc{mkqa,
    title = {MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering},
    author = {Shayne Longpre and Yi Lu and Joachim Daiber},
    year = {2020},
    URL = {https://arxiv.org/pdf/2007.15207.pdf}
}
```

### Contributions

Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.
task_categories:question-answering task_ids:open-domain-qa annotations_creators:crowdsourced language_creators:found multilinguality:multilingual multilinguality:translation size_categories:10K<n<100K source_datasets:extended|natural_questions source_datasets:original language:ar language:da language:de language:en language:es language:fi language:fr language:he language:hu language:it language:ja language:km language:ko language:ms language:nl language:no language:pl language:pt language:ru language:sv language:th language:tr language:vi language:zh license:cc-by-3.0 arxiv:2007.15207 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["ar", "da", "de", "en", "es", "fi", "fr", "he", "hu", "it", "ja", "km", "ko", "ms", "nl", "no", "pl", "pt", "ru", "sv", "th", "tr", "vi", "zh"], "license": ["cc-by-3.0"], "multilinguality": ["multilingual", "translation"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|natural_questions", "original"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa"], "paperswithcode_id": "mkqa", "pretty_name": "Multilingual Knowledge Questions and Answers", "dataset_info": {"features": [{"name": "example_id", "dtype": "string"}, {"name": "queries", "struct": [{"name": "ar", "dtype": "string"}, {"name": "da", "dtype": "string"}, {"name": "de", "dtype": "string"}, {"name": "en", "dtype": "string"}, {"name": "es", "dtype": "string"}, {"name": "fi", "dtype": "string"}, {"name": "fr", "dtype": "string"}, {"name": "he", "dtype": "string"}, {"name": "hu", "dtype": "string"}, {"name": "it", "dtype": "string"}, {"name": "ja", "dtype": "string"}, {"name": "ko", "dtype": "string"}, {"name": "km", "dtype": "string"}, {"name": "ms", "dtype": "string"}, {"name": "nl", "dtype": "string"}, {"name": "no", "dtype": "string"}, {"name": "pl", "dtype": "string"}, {"name": "pt", "dtype": "string"}, {"name": "ru", "dtype": "string"}, {"name": "sv", "dtype": "string"}, {"name": "th", "dtype": "string"}, {"name": "tr", "dtype": "string"}, {"name": "vi", "dtype": "string"}, {"name": "zh_cn", "dtype": "string"}, {"name": "zh_hk", "dtype": "string"}, {"name": "zh_tw", "dtype": "string"}]}, {"name": "query", "dtype": "string"}, {"name": "answers", "struct": [{"name": "ar", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "da", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "de", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "en", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "es", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "fi", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "fr", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "he", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "hu", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "it", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "ja", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "ko", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "km", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "ms", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "nl", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "no", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "pl", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "pt", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "ru", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "sv", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "th", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "tr", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "vi", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "zh_cn", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "zh_hk", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "zh_tw", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}]}], "config_name": "mkqa", "splits": [{"name": "train", "num_bytes": 36005650, "num_examples": 10000}], "download_size": 11903948, "dataset_size": 36005650}}
TAGS
#task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-found #multilinguality-multilingual #multilinguality-translation #size_categories-10K<n<100K #source_datasets-extended|natural_questions #source_datasets-original #language-Arabic #language-Danish #language-German #language-English #language-Spanish #language-Finnish #language-French #language-Hebrew #language-Hungarian #language-Italian #language-Japanese #language-Khmer #language-Korean #language-Malay (macrolanguage) #language-Dutch #language-Norwegian #language-Polish #language-Portuguese #language-Russian #language-Swedish #language-Thai #language-Turkish #language-Vietnamese #language-Chinese #license-cc-by-3.0 #arxiv-2007.15207 #region-us 

Dataset Card for MKQA: Multilingual Knowledge Questions & Answers
=================================================================


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:
* Paper:


### Dataset Summary


MKQA contains 10,000 queries sampled from the Google Natural Questions dataset.


For each query we collect new passage-independent answers.
These queries and answers are then human translated into 25 Non-English languages.


### Supported Tasks and Leaderboards


'question-answering'


### Languages



Dataset Structure
-----------------


### Data Instances


An example from the data set looks as follows:


### Data Fields


Each example in the dataset contains the unique Natural Questions 'example\_id', the original English 'query', and then 'queries' and 'answers' in 26 languages.
Each answer is labelled with an answer type. The breakdown is:



For each language, there can be more than one acceptable textual answer, in order to capture a variety of possible valid answers.


Detailed explanation of fields taken from here


when 'entity' field is not available it is set to an empty string ''.
when 'aliases' field is not available it is set to an empty list [].


### Data Splits


* Train: 10000


Dataset Creation
----------------


### Curation Rationale


### Source Data


Google Natural Questions dataset


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### 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


CC BY-SA 3.0


### Contributions


Thanks to @cceyda for adding this dataset.



### Dataset Summary


MKQA contains 10,000 queries sampled from the Google Natural Questions dataset.


For each query we collect new passage-independent answers.
These queries and answers are then human translated into 25 Non-English languages. ### Supported Tasks and Leaderboards


'question-answering' ### Languages



Dataset Structure
----------------- ### Data Instances


An example from the data set looks as follows: ### Data Fields


Each example in the dataset contains the unique Natural Questions 'example\_id', the original English 'query', and then 'queries' and 'answers' in 26 languages.
Each answer is labelled with an answer type. The breakdown is:



For each language, there can be more than one acceptable textual answer, in order to capture a variety of possible valid answers.


Detailed explanation of fields taken from here


when 'entity' field is not available it is set to an empty string ''.
when 'aliases' field is not available it is set to an empty list []. ### Data Splits


* Train: 10000


Dataset Creation
---------------- ### Curation Rationale ### Source Data


Google Natural Questions dataset #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### 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


CC BY-SA 3.0 ### Contributions


Thanks to @cceyda for adding this dataset.
TAGS
#task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-found #multilinguality-multilingual #multilinguality-translation #size_categories-10K<n<100K #source_datasets-extended|natural_questions #source_datasets-original #language-Arabic #language-Danish #language-German #language-English #language-Spanish #language-Finnish #language-French #language-Hebrew #language-Hungarian #language-Italian #language-Japanese #language-Khmer #language-Korean #language-Malay (macrolanguage) #language-Dutch #language-Norwegian #language-Polish #language-Portuguese #language-Russian #language-Swedish #language-Thai #language-Turkish #language-Vietnamese #language-Chinese #license-cc-by-3.0 #arxiv-2007.15207 #region-us 
 ### Dataset Summary


MKQA contains 10,000 queries sampled from the Google Natural Questions dataset.


For each query we collect new passage-independent answers.
These queries and answers are then human translated into 25 Non-English languages. ### Supported Tasks and Leaderboards


'question-answering' ### Languages



Dataset Structure
----------------- ### Data Instances


An example from the data set looks as follows: ### Data Fields


Each example in the dataset contains the unique Natural Questions 'example\_id', the original English 'query', and then 'queries' and 'answers' in 26 languages.
Each answer is labelled with an answer type. The breakdown is:



For each language, there can be more than one acceptable textual answer, in order to capture a variety of possible valid answers.


Detailed explanation of fields taken from here


when 'entity' field is not available it is set to an empty string ''.
when 'aliases' field is not available it is set to an empty list []. ### Data Splits


* Train: 10000


Dataset Creation
---------------- ### Curation Rationale ### Source Data


Google Natural Questions dataset #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### 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


CC BY-SA 3.0 ### Contributions


Thanks to @cceyda for adding this dataset.
passage: TAGS
#task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-found #multilinguality-multilingual #multilinguality-translation #size_categories-10K<n<100K #source_datasets-extended|natural_questions #source_datasets-original #language-Arabic #language-Danish #language-German #language-English #language-Spanish #language-Finnish #language-French #language-Hebrew #language-Hungarian #language-Italian #language-Japanese #language-Khmer #language-Korean #language-Malay (macrolanguage) #language-Dutch #language-Norwegian #language-Polish #language-Portuguese #language-Russian #language-Swedish #language-Thai #language-Turkish #language-Vietnamese #language-Chinese #license-cc-by-3.0 #arxiv-2007.15207 #region-us 
### Dataset Summary


MKQA contains 10,000 queries sampled from the Google Natural Questions dataset.


For each query we collect new passage-independent answers.
These queries and answers are then human translated into 25 Non-English languages.### Supported Tasks and Leaderboards


'question-answering'### Languages



Dataset Structure
-----------------### Data Instances


An example from the data set looks as follows:### Data Fields


Each example in the dataset contains the unique Natural Questions 'example\_id', the original English 'query', and then 'queries' and 'answers' in 26 languages.
Each answer is labelled with an answer type. The breakdown is:



For each language, there can be more than one acceptable textual answer, in order to capture a variety of possible valid answers.


Detailed explanation of fields taken from here


when 'entity' field is not available it is set to an empty string ''.
when 'aliases' field is not available it is set to an empty list [].