Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering

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

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

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.