| --- |
| 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 |
| --- |
| |
| # Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering |
|
|
| ## 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/amazon-science/mintaka |
| - **Repository:** https://github.com/amazon-science/mintaka |
| - **Paper:** https://aclanthology.org/2022.coling-1.138/ |
| - **Point of Contact:** [GitHub](https://github.com/amazon-science/mintaka) |
|
|
| ### 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. |
|
|
| ```json |
| { |
| "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 |
| [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 |
|
|
| 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. |
|
|
| ```latex |
| @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](https://github.com/afaji) for adding this dataset. |
