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cosmos_qa

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Dataset Card for "cosmos_qa"

Dataset Summary

Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

Data Instances

default

Size of downloaded dataset files: 24.40 MB
Size of the generated dataset: 24.51 MB
Total amount of disk used: 48.91 MB

An example of 'validation' looks as follows.

This example was too long and was cropped:

{
    "answer0": "If he gets married in the church he wo nt have to get a divorce .",
    "answer1": "He wants to get married to a different person .",
    "answer2": "He wants to know if he does nt like this girl can he divorce her ?",
    "answer3": "None of the above choices .",
    "context": "\"Do i need to go for a legal divorce ? I wanted to marry a woman but she is not in the same religion , so i am not concern of th...",
    "id": "3BFF0DJK8XA7YNK4QYIGCOG1A95STE##3180JW2OT5AF02OISBX66RFOCTG5J7##A2LTOS0AZ3B28A##Blog_56156##q1_a1##378G7J1SJNCDAAIN46FM2P7T6KZEW2",
    "label": 1,
    "question": "Why is this person asking about divorce ?"
}

Data Fields

The data fields are the same among all splits.

default

id: a string feature.
context: a string feature.
question: a string feature.
answer0: a string feature.
answer1: a string feature.
answer2: a string feature.
answer3: a string feature.
label: a int32 feature.

Data Splits

name	train	validation	test
default	25262	2985	6963

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

As reported via email by Yejin Choi, the dataset is licensed under CC BY 4.0 license.

Citation Information

@inproceedings{huang-etal-2019-cosmos,
    title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning",
    author = "Huang, Lifu  and
      Le Bras, Ronan  and
      Bhagavatula, Chandra  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1243",
    doi = "10.18653/v1/D19-1243",
    pages = "2391--2401",
}