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- all/mintaka-test.parquet +3 -0
- all/mintaka-train.parquet +3 -0
- all/mintaka-validation.parquet +3 -0
- ar/mintaka-test.parquet +3 -0
- ar/mintaka-train.parquet +3 -0
- ar/mintaka-validation.parquet +3 -0
- de/mintaka-test.parquet +3 -0
- de/mintaka-train.parquet +3 -0
- de/mintaka-validation.parquet +3 -0
- en/mintaka-test.parquet +3 -0
- en/mintaka-train.parquet +3 -0
- en/mintaka-validation.parquet +3 -0
- es/mintaka-test.parquet +3 -0
- es/mintaka-train.parquet +3 -0
- es/mintaka-validation.parquet +3 -0
- fr/mintaka-test.parquet +3 -0
- fr/mintaka-train.parquet +3 -0
- fr/mintaka-validation.parquet +3 -0
- hi/mintaka-test.parquet +3 -0
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- it/mintaka-test.parquet +3 -0
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- it/mintaka-validation.parquet +3 -0
- ja/mintaka-test.parquet +3 -0
- ja/mintaka-train.parquet +3 -0
- ja/mintaka-validation.parquet +3 -0
- mintaka.py +0 -177
- pt/mintaka-test.parquet +3 -0
- pt/mintaka-train.parquet +3 -0
- pt/mintaka-validation.parquet +3 -0
- test_mintaka.py +0 -16
README.md
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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
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- ja-JP
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- hi-HI
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- pt-PT
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- en-EN
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- es-ES
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- it-IT
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- fr-FR
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---
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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.
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all/mintaka-test.parquet
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es/mintaka-test.parquet
ADDED
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es/mintaka-validation.parquet
ADDED
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fr/mintaka-test.parquet
ADDED
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fr/mintaka-train.parquet
ADDED
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fr/mintaka-validation.parquet
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hi/mintaka-test.parquet
ADDED
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hi/mintaka-train.parquet
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hi/mintaka-validation.parquet
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it/mintaka-test.parquet
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it/mintaka-train.parquet
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|
it/mintaka-validation.parquet
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|
ja/mintaka-test.parquet
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|
ja/mintaka-validation.parquet
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|
mintaka.py
DELETED
@@ -1,177 +0,0 @@
|
|
1 |
-
# coding=utf-8
|
2 |
-
|
3 |
-
"""Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering"""
|
4 |
-
|
5 |
-
import json
|
6 |
-
import datasets
|
7 |
-
|
8 |
-
logger = datasets.logging.get_logger(__name__)
|
9 |
-
|
10 |
-
_DESCRIPTION = """\
|
11 |
-
Mintaka is a complex, natural, and multilingual dataset designed for experimenting with end-to-end
|
12 |
-
question-answering models. Mintaka is composed of 20,000 question-answer pairs collected in English,
|
13 |
-
annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian,
|
14 |
-
Japanese, Portuguese, and Spanish for a total of 180,000 samples.
|
15 |
-
Mintaka includes 8 types of complex questions, including superlative, intersection, and multi-hop questions,
|
16 |
-
which were naturally elicited from crowd workers.
|
17 |
-
"""
|
18 |
-
|
19 |
-
_CITATION = """\
|
20 |
-
@inproceedings{sen-etal-2022-mintaka,
|
21 |
-
title = "Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering",
|
22 |
-
author = "Sen, Priyanka and Aji, Alham Fikri and Saffari, Amir",
|
23 |
-
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
|
24 |
-
month = oct,
|
25 |
-
year = "2022",
|
26 |
-
address = "Gyeongju, Republic of Korea",
|
27 |
-
publisher = "International Committee on Computational Linguistics",
|
28 |
-
url = "https://aclanthology.org/2022.coling-1.138",
|
29 |
-
pages = "1604--1619"
|
30 |
-
}
|
31 |
-
"""
|
32 |
-
|
33 |
-
_LICENSE = """\
|
34 |
-
Copyright Amazon.com Inc. or its affiliates.
|
35 |
-
Attribution 4.0 International
|
36 |
-
"""
|
37 |
-
|
38 |
-
_TRAIN_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_train.json"
|
39 |
-
_DEV_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_dev.json"
|
40 |
-
_TEST_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_test.json"
|
41 |
-
|
42 |
-
|
43 |
-
_LANGUAGES = ['en', 'ar', 'de', 'ja', 'hi', 'pt', 'es', 'it', 'fr']
|
44 |
-
|
45 |
-
_ALL = "all"
|
46 |
-
|
47 |
-
class Mintaka(datasets.GeneratorBasedBuilder):
|
48 |
-
"""Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering"""
|
49 |
-
|
50 |
-
BUILDER_CONFIGS = [
|
51 |
-
datasets.BuilderConfig(
|
52 |
-
name = name,
|
53 |
-
version = datasets.Version("1.0.0"),
|
54 |
-
description = f"Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering for {name}",
|
55 |
-
) for name in _LANGUAGES
|
56 |
-
]
|
57 |
-
|
58 |
-
BUILDER_CONFIGS.append(datasets.BuilderConfig(
|
59 |
-
name = _ALL,
|
60 |
-
version = datasets.Version("1.0.0"),
|
61 |
-
description = f"Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering",
|
62 |
-
))
|
63 |
-
|
64 |
-
DEFAULT_CONFIG_NAME = 'en'
|
65 |
-
|
66 |
-
def _info(self):
|
67 |
-
return datasets.DatasetInfo(
|
68 |
-
description=_DESCRIPTION,
|
69 |
-
features=datasets.Features(
|
70 |
-
{
|
71 |
-
"id": datasets.Value("string"),
|
72 |
-
"lang": datasets.Value("string"),
|
73 |
-
"question": datasets.Value("string"),
|
74 |
-
"answerText": datasets.Value("string"),
|
75 |
-
"category": datasets.Value("string"),
|
76 |
-
"complexityType": datasets.Value("string"),
|
77 |
-
"questionEntity": [{
|
78 |
-
"name": datasets.Value("string"),
|
79 |
-
"entityType": datasets.Value("string"),
|
80 |
-
"label": datasets.Value("string"),
|
81 |
-
"mention": datasets.Value("string"),
|
82 |
-
"span": [datasets.Value("int32")],
|
83 |
-
}],
|
84 |
-
"answerEntity": [{
|
85 |
-
"name": datasets.Value("string"),
|
86 |
-
"label": datasets.Value("string"),
|
87 |
-
}]
|
88 |
-
},
|
89 |
-
),
|
90 |
-
supervised_keys=None,
|
91 |
-
citation=_CITATION,
|
92 |
-
license=_LICENSE,
|
93 |
-
)
|
94 |
-
|
95 |
-
def _split_generators(self, dl_manager):
|
96 |
-
return [
|
97 |
-
datasets.SplitGenerator(
|
98 |
-
name=datasets.Split.TRAIN,
|
99 |
-
gen_kwargs={
|
100 |
-
"file": dl_manager.download_and_extract(_TRAIN_URL),
|
101 |
-
"lang": self.config.name,
|
102 |
-
}
|
103 |
-
),
|
104 |
-
datasets.SplitGenerator(
|
105 |
-
name=datasets.Split.VALIDATION,
|
106 |
-
gen_kwargs={
|
107 |
-
"file": dl_manager.download_and_extract(_DEV_URL),
|
108 |
-
"lang": self.config.name,
|
109 |
-
}
|
110 |
-
),
|
111 |
-
datasets.SplitGenerator(
|
112 |
-
name=datasets.Split.TEST,
|
113 |
-
gen_kwargs={
|
114 |
-
"file": dl_manager.download_and_extract(_TEST_URL),
|
115 |
-
"lang": self.config.name,
|
116 |
-
}
|
117 |
-
),
|
118 |
-
]
|
119 |
-
|
120 |
-
def _generate_examples(self, file, lang):
|
121 |
-
if lang == _ALL:
|
122 |
-
langs = _LANGUAGES
|
123 |
-
else:
|
124 |
-
langs = [lang]
|
125 |
-
|
126 |
-
key_ = 0
|
127 |
-
|
128 |
-
logger.info("⏳ Generating examples from = %s", ", ".join(lang))
|
129 |
-
|
130 |
-
with open(file, encoding='utf-8') as json_file:
|
131 |
-
data = json.load(json_file)
|
132 |
-
for lang in langs:
|
133 |
-
for sample in data:
|
134 |
-
questionEntity = [
|
135 |
-
{
|
136 |
-
"name": str(qe["name"]),
|
137 |
-
"entityType": qe["entityType"],
|
138 |
-
"label": qe["label"] if "label" in qe else "",
|
139 |
-
# span only applies for English question
|
140 |
-
"mention": qe["mention"] if lang == "en" else None,
|
141 |
-
"span": qe["span"] if lang == "en" else [],
|
142 |
-
} for qe in sample["questionEntity"]
|
143 |
-
]
|
144 |
-
|
145 |
-
answers = []
|
146 |
-
if sample['answer']["answerType"] == "entity" and sample['answer']['answer'] is not None:
|
147 |
-
answers = sample['answer']['answer']
|
148 |
-
elif sample['answer']["answerType"] == "numerical" and "supportingEnt" in sample["answer"]:
|
149 |
-
answers = sample['answer']['supportingEnt']
|
150 |
-
|
151 |
-
# helper to get language for the corresponding language
|
152 |
-
def get_label(labels, lang):
|
153 |
-
if lang in labels:
|
154 |
-
return labels[lang]
|
155 |
-
if 'en' in labels:
|
156 |
-
return labels['en']
|
157 |
-
return None
|
158 |
-
|
159 |
-
answerEntity = [
|
160 |
-
{
|
161 |
-
"name": str(ae["name"]),
|
162 |
-
"label": get_label(ae["label"], lang),
|
163 |
-
} for ae in answers
|
164 |
-
]
|
165 |
-
|
166 |
-
yield key_, {
|
167 |
-
"id": sample["id"],
|
168 |
-
"lang": lang,
|
169 |
-
"question": sample["question"] if lang == 'en' else sample['translations'][lang],
|
170 |
-
"answerText": sample["answer"]["mention"],
|
171 |
-
"category": sample["category"],
|
172 |
-
"complexityType": sample["complexityType"],
|
173 |
-
"questionEntity": questionEntity,
|
174 |
-
"answerEntity": answerEntity,
|
175 |
-
}
|
176 |
-
|
177 |
-
key_ += 1
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
pt/mintaka-test.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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2 |
+
oid sha256:e20d27fd4d97b44ee9ef8131915fe147807f371a6db138e9a4d7185c643ac34b
|
3 |
+
size 403900
|
pt/mintaka-train.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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2 |
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oid sha256:e0934012202bdff4ea6f9c85d02bb0db3a7d79f72bd00e80edd4cf430c5dd993
|
3 |
+
size 1252021
|
pt/mintaka-validation.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:2f586c25db99fb968f44d182fcc2348db4f5098b8e38d1f3de1a5ec26f5e46e4
|
3 |
+
size 217004
|
test_mintaka.py
DELETED
@@ -1,16 +0,0 @@
|
|
1 |
-
from datasets import load_dataset
|
2 |
-
|
3 |
-
source = "AmazonScience/mintaka"
|
4 |
-
|
5 |
-
#dataset = load_dataset(source, "all", download_mode="force_redownload")
|
6 |
-
dataset = load_dataset(source, "all")
|
7 |
-
|
8 |
-
print(dataset)
|
9 |
-
print(dataset["train"][0])
|
10 |
-
print(dataset["train"][0:10]['question'])
|
11 |
-
|
12 |
-
|
13 |
-
dataset = load_dataset(source, "en")
|
14 |
-
dataset = load_dataset(source, "ar")
|
15 |
-
|
16 |
-
|
|
|
|
|
|
|
|
|
|
|
|
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