|
--- |
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annotations_creators: |
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- expert-generated |
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language: |
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- en |
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language_creators: |
|
- expert-generated |
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license: |
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- cc-by-4.0 |
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multilinguality: |
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- monolingual |
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pretty_name: Modified Winograd Schema Challenge (MWSC) |
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size_categories: |
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- n<1K |
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source_datasets: |
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- extended|winograd_wsc |
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task_categories: |
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- multiple-choice |
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task_ids: |
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- multiple-choice-coreference-resolution |
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paperswithcode_id: null |
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dataset_info: |
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features: |
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- name: sentence |
|
dtype: string |
|
- name: question |
|
dtype: string |
|
- name: options |
|
sequence: string |
|
- name: answer |
|
dtype: string |
|
splits: |
|
- name: train |
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num_bytes: 11022 |
|
num_examples: 80 |
|
- name: test |
|
num_bytes: 15220 |
|
num_examples: 100 |
|
- name: validation |
|
num_bytes: 13109 |
|
num_examples: 82 |
|
download_size: 19197 |
|
dataset_size: 39351 |
|
--- |
|
|
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# The modified Winograd Schema Challenge (MWSC) |
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|
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## Dataset Description |
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|
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- **Homepage:** [http://decanlp.com](http://decanlp.com) |
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- **Repository:** https://github.com/salesforce/decaNLP |
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- **Paper:** [The Natural Language Decathlon: Multitask Learning as Question Answering](https://arxiv.org/abs/1806.08730) |
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- **Point of Contact:** [Bryan McCann](mailto:bmccann@salesforce.com), [Nitish Shirish Keskar](mailto:nkeskar@salesforce.com) |
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- **Size of downloaded dataset files:** 19.20 kB |
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- **Size of the generated dataset:** 39.35 kB |
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- **Total amount of disk used:** 58.55 kB |
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|
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### Dataset Summary |
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|
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Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context. |
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This Modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing. |
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|
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## Dataset Structure |
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|
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### Data Instances |
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|
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#### default |
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|
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- **Size of downloaded dataset files:** 0.02 MB |
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- **Size of the generated dataset:** 0.04 MB |
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- **Total amount of disk used:** 0.06 MB |
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|
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An example looks as follows: |
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``` |
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{ |
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"sentence": "The city councilmen refused the demonstrators a permit because they feared violence.", |
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"question": "Who feared violence?", |
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"options": [ "councilmen", "demonstrators" ], |
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"answer": "councilmen" |
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} |
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``` |
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|
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### Data Fields |
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|
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The data fields are the same among all splits. |
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|
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#### default |
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- `sentence`: a `string` feature. |
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- `question`: a `string` feature. |
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- `options`: a `list` of `string` features. |
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- `answer`: a `string` feature. |
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|
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### Data Splits |
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|
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| name |train|validation|test| |
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|-------|----:|---------:|---:| |
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|default| 80| 82| 100| |
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|
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### Licensing Information |
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|
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Our code for running decaNLP has been open sourced under BSD-3-Clause. |
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|
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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. |
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|
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From the [Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html): |
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> Both versions of the collections are licenced under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/). |
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### Citation Information |
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|
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If you use this in your work, please cite: |
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``` |
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@inproceedings{10.5555/3031843.3031909, |
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author = {Levesque, Hector J. and Davis, Ernest and Morgenstern, Leora}, |
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title = {The Winograd Schema Challenge}, |
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year = {2012}, |
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isbn = {9781577355601}, |
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publisher = {AAAI Press}, |
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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.}, |
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booktitle = {Proceedings of the Thirteenth International Conference on Principles of Knowledge Representation and Reasoning}, |
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pages = {552–561}, |
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numpages = {10}, |
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location = {Rome, Italy}, |
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series = {KR'12} |
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} |
|
|
|
@article{McCann2018decaNLP, |
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title={The Natural Language Decathlon: Multitask Learning as Question Answering}, |
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author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher}, |
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journal={arXiv preprint arXiv:1806.08730}, |
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year={2018} |
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} |
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``` |
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### Contributions |
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|
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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. |
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