Datasets:

coref-data
/

mwsc_raw

	---
	license: cc-by-4.0
	---

	# The Modified Winograd Schema Challenge (MWSC)

	## Dataset Description

	- Homepage: [http://decanlp.com](http://decanlp.com)
	- Repository: https://github.com/salesforce/decaNLP
	- Paper: [The Natural Language Decathlon: Multitask Learning as Question Answering](https://arxiv.org/abs/1806.08730)
	- Point of Contact: [Bryan McCann](mailto:bmccann@salesforce.com), [Nitish Shirish Keskar](mailto:nkeskar@salesforce.com)
	- Size of downloaded dataset files: 19.20 kB
	- Size of the generated dataset: 39.35 kB
	- Total amount of disk used: 58.55 kB

	### 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.

	## 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`: a `string` feature.
	- `question`: a `string` feature.
	- `options`: a `list` of `string` features.
	- `answer`: a `string` feature.

	### Data Splits

	\| name \|train\|validation\|test\|
	\|-------\|----:\|---------:\|---:\|
	\|default\| 80\| 82\| 100\|

	### 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](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html):
	> Both versions of the collections are licenced under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).

	### Citation Information

	If you use this in your work, please cite:
	```
	@inproceedings{10.5555/3031843.3031909,
	author = {Levesque, Hector J. and Davis, Ernest and Morgenstern, Leora},
	title = {The Winograd Schema Challenge},
	year = {2012},
	isbn = {9781577355601},
	publisher = {AAAI Press},
	abstract = {In this paper, we present an alternative to the Turing Test that has some conceptual and practical advantages. A Wino-grad schema is a pair of sentences that differ only in one or two words and that contain a referential ambiguity that is resolved in opposite directions in the two sentences. We have compiled a collection of Winograd schemas, designed so that the correct answer is obvious to the human reader, but cannot easily be found using selectional restrictions or statistical techniques over text corpora. A contestant in the Winograd Schema Challenge is presented with a collection of one sentence from each pair, and required to achieve human-level accuracy in choosing the correct disambiguation.},
	booktitle = {Proceedings of the Thirteenth International Conference on Principles of Knowledge Representation and Reasoning},
	pages = {552–561},
	numpages = {10},
	location = {Rome, Italy},
	series = {KR'12}
	}

	@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](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@ghomasHudson](https://github.com/ghomasHudson), [@lhoestq](https://github.com/lhoestq) for adding this dataset.