Datasets:
metadata
annotations_creators:
- expert-generated
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- ar
- de
- ja
- hi
- pt
- en
- es
- it
- fr
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: mintaka
pretty_name: Mintaka
language_bcp47:
- ar-SA
- de-DE
- ja-JP
- hi-HI
- pt-PT
- en-EN
- es-ES
- it-IT
- fr-FR
configs:
- config_name: all
data_files:
- split: train
path: all/train-*
- split: validation
path: all/validation-*
- split: test
path: all/test-*
- config_name: ar
data_files:
- split: train
path: ar/train-*
- split: validation
path: ar/validation-*
- split: test
path: ar/test-*
- config_name: de
data_files:
- split: train
path: de/train-*
- split: validation
path: de/validation-*
- split: test
path: de/test-*
- config_name: en
data_files:
- split: train
path: en/train-*
- split: validation
path: en/validation-*
- split: test
path: en/test-*
default: true
- config_name: es
data_files:
- split: train
path: es/train-*
- split: validation
path: es/validation-*
- split: test
path: es/test-*
- config_name: fr
data_files:
- split: train
path: fr/train-*
- split: validation
path: fr/validation-*
- split: test
path: fr/test-*
- config_name: hi
data_files:
- split: train
path: hi/train-*
- split: validation
path: hi/validation-*
- split: test
path: hi/test-*
- config_name: it
data_files:
- split: train
path: it/train-*
- split: validation
path: it/validation-*
- split: test
path: it/test-*
- config_name: ja
data_files:
- split: train
path: ja/train-*
- split: validation
path: ja/validation-*
- split: test
path: ja/test-*
- config_name: pt
data_files:
- split: train
path: pt/train-*
- split: validation
path: pt/validation-*
- split: test
path: pt/test-*
dataset_info:
- config_name: all
features:
- name: id
dtype: string
- name: lang
dtype: string
- name: question
dtype: string
- name: answerText
dtype: string
- name: category
dtype: string
- name: complexityType
dtype: string
- name: questionEntity
list:
- name: name
dtype: string
- name: entityType
dtype: string
- name: label
dtype: string
- name: mention
dtype: string
- name: span
list: int32
- name: answerEntity
list:
- name: name
dtype: string
- name: label
dtype: string
splits:
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- name: validation
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- name: test
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download_size: 17797002
dataset_size: 46616665
- config_name: ar
features:
- name: id
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- name: lang
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- name: question
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- name: answerText
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- name: category
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- name: complexityType
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- name: questionEntity
list:
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- name: entityType
dtype: string
- name: label
dtype: string
- name: mention
dtype: string
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- name: label
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- config_name: de
features:
- name: id
dtype: string
- name: lang
dtype: string
- name: question
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- name: answerText
dtype: string
- name: category
dtype: string
- name: complexityType
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- name: questionEntity
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- config_name: en
features:
- name: id
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- name: lang
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- name: question
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- name: answerText
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- name: category
dtype: string
- name: complexityType
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- name: questionEntity
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- name: entityType
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- config_name: es
features:
- name: id
dtype: string
- name: lang
dtype: string
- name: question
dtype: string
- name: answerText
dtype: string
- name: category
dtype: string
- name: complexityType
dtype: string
- name: questionEntity
list:
- name: name
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- name: entityType
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- name: label
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- config_name: fr
features:
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- name: lang
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- name: question
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- name: answerText
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- name: category
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- name: question
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- name: category
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- name: lang
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- name: question
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- name: answerText
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dataset_size: 4691892
Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://github.com/amazon-science/mintaka
- Repository: https://github.com/amazon-science/mintaka
- Paper: https://aclanthology.org/2022.coling-1.138/
- Point of Contact: GitHub
Dataset Summary
Mintaka is a complex, natural, and multilingual question answering (QA) dataset composed of 20,000 question-answer pairs elicited from MTurk workers and annotated with Wikidata question and answer entities. Full details on the Mintaka dataset can be found in our paper: https://aclanthology.org/2022.coling-1.138/
To build Mintaka, we explicitly collected questions in 8 complexity types, as well as generic questions:
- Count (e.g., Q: How many astronauts have been elected to Congress? A: 4)
- Comparative (e.g., Q: Is Mont Blanc taller than Mount Rainier? A: Yes)
- Superlative (e.g., Q: Who was the youngest tribute in the Hunger Games? A: Rue)
- Ordinal (e.g., Q: Who was the last Ptolemaic ruler of Egypt? A: Cleopatra)
- Multi-hop (e.g., Q: Who was the quarterback of the team that won Super Bowl 50? A: Peyton Manning)
- Intersection (e.g., Q: Which movie was directed by Denis Villeneuve and stars Timothee Chalamet? A: Dune)
- Difference (e.g., Q: Which Mario Kart game did Yoshi not appear in? A: Mario Kart Live: Home Circuit)
- Yes/No (e.g., Q: Has Lady Gaga ever made a song with Ariana Grande? A: Yes.)
- Generic (e.g., Q: Where was Michael Phelps born? A: Baltimore, Maryland)
- We collected questions about 8 categories: Movies, Music, Sports, Books, Geography, Politics, Video Games, and History
Mintaka is one of the first large-scale complex, natural, and multilingual datasets that can be used for end-to-end question-answering models.
Supported Tasks and Leaderboards
The dataset can be used to train a model for question answering. To ensure comparability, please refer to our evaluation script here: https://github.com/amazon-science/mintaka#evaluation
Languages
All questions were written in English and translated into 8 additional languages: Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish.
Dataset Structure
Data Instances
An example of 'train' looks as follows.
{
"id": "a9011ddf",
"lang": "en",
"question": "What is the seventh tallest mountain in North America?",
"answerText": "Mount Lucania",
"category": "geography",
"complexityType": "ordinal",
"questionEntity":
[
{
"name": "Q49",
"entityType": "entity",
"label": "North America",
"mention": "North America",
"span": [40, 53]
},
{
"name": 7,
"entityType": "ordinal",
"mention": "seventh",
"span": [12, 19]
}
],
"answerEntity":
[
{
"name": "Q1153188",
"label": "Mount Lucania",
}
],
}
Data Fields
The data fields are the same among all splits.
id: a unique ID for the given sample.
lang: the language of the question.
question: the original question elicited in the corresponding language.
answerText: the original answer text elicited in English.
category: the category of the question. Options are: geography, movies, history, books, politics, music, videogames, or sports
complexityType: the complexity type of the question. Options are: ordinal, intersection, count, superlative, yesno comparative, multihop, difference, or generic
questionEntity: a list of annotated question entities identified by crowd workers.
{
"name": The Wikidata Q-code or numerical value of the entity
"entityType": The type of the entity. Options are:
entity, cardinal, ordinal, date, time, percent, quantity, or money
"label": The label of the Wikidata Q-code
"mention": The entity as it appears in the English question text. Will be empty for non-English samples.
"span": The start and end characters of the mention in the English question text. Will be empty for non-English samples.
}
answerEntity: a list of annotated answer entities identified by crowd workers.
{
"name": The Wikidata Q-code or numerical value of the entity
"label": The label of the Wikidata Q-code
}
Data Splits
For each language, we split into train (14,000 samples), dev (2,000 samples), and test (4,000 samples) sets.
Personal and Sensitive Information
The corpora is free of personal or sensitive information.
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Amazon Alexa AI.
Licensing Information
This project is licensed under the CC-BY-4.0 License.
Citation Information
Please cite the following papers when using this dataset.
@inproceedings{sen-etal-2022-mintaka,
title = "Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering",
author = "Sen, Priyanka and
Aji, Alham Fikri and
Saffari, Amir",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.138",
pages = "1604--1619"
}
Contributions
Thanks to @afaji for adding this dataset.