Datasets:
Tasks:
Multiple Choice
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
10K<n<100K
ArXiv:
License:
metadata
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: CosmosQA
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
paperswithcode_id: cosmosqa
dataset_info:
features:
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answer0
dtype: string
- name: answer1
dtype: string
- name: answer2
dtype: string
- name: answer3
dtype: string
- name: label
dtype: int32
splits:
- name: train
num_bytes: 17159918
num_examples: 25262
- name: test
num_bytes: 5121479
num_examples: 6963
- name: validation
num_bytes: 2186987
num_examples: 2985
download_size: 24399475
dataset_size: 24468384
Dataset Card for "cosmos_qa"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://wilburone.github.io/cosmos/
- Repository: https://github.com/wilburOne/cosmosqa/
- Paper: Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning
- Point of Contact: Lifu Huang
- Size of downloaded dataset files: 23.27 MB
- Size of the generated dataset: 23.37 MB
- Total amount of disk used: 46.64 MB
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
Languages
Dataset Structure
Data Instances
default
- Size of downloaded dataset files: 23.27 MB
- Size of the generated dataset: 23.37 MB
- Total amount of disk used: 46.64 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
: astring
feature.context
: astring
feature.question
: astring
feature.answer0
: astring
feature.answer1
: astring
feature.answer2
: astring
feature.answer3
: astring
feature.label
: aint32
feature.
Data Splits
name | train | validation | test |
---|---|---|---|
default | 25262 | 2985 | 6963 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
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",
}
Contributions
Thanks to @patrickvonplaten, @lewtun, @albertvillanova, @thomwolf for adding this dataset.