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Dataset Card for The modified Winograd Schema Challenge (MWSC)
Dataset Summary
Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context. This Modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
default
- Size of downloaded dataset files: 0.02 MB
- Size of the generated dataset: 0.04 MB
- Total amount of disk used: 0.06 MB
An example looks as follows:
{
"sentence": "The city councilmen refused the demonstrators a permit because they feared violence.",
"question": "Who feared violence?",
"options": [ "councilmen", "demonstrators" ],
"answer": "councilmen"
}
Data Fields
The data fields are the same among all splits.
default
sentence: astringfeature.question: astringfeature.options: alistofstringfeatures.answer: astringfeature.
Data Splits
| name | train | validation | test |
|---|---|---|---|
| default | 80 | 82 | 100 |
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
Our code for running decaNLP has been open sourced under BSD-3-Clause.
We chose to restrict decaNLP to datasets that were free and publicly accessible for research, but you should check their individual terms if you deviate from this use case.
From the Winograd Schema Challenge:
Both versions of the collections are licenced under a Creative Commons Attribution 4.0 International License.
Citation Information
If you use this in your work, please cite:
@article{McCann2018decaNLP,
title={The Natural Language Decathlon: Multitask Learning as Question Answering},
author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher},
journal={arXiv preprint arXiv:1806.08730},
year={2018}
}
Contributions
Thanks to @thomwolf, @lewtun, @ghomasHudson, @lhoestq for adding this dataset.
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