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