Datasets:
Tasks:
Multiple Choice
Sub-tasks:
multiple-choice-coreference-resolution
Languages:
English
Size:
n<1K
License:
| annotations_creators: | |
| - expert-generated | |
| language_creators: | |
| - expert-generated | |
| language: | |
| - en | |
| license: | |
| - cc-by-4.0 | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - n<1K | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - multiple-choice | |
| task_ids: | |
| - multiple-choice-coreference-resolution | |
| paperswithcode_id: wsc | |
| pretty_name: Winograd Schema Challenge | |
| dataset_info: | |
| - config_name: wsc285 | |
| features: | |
| - name: text | |
| dtype: string | |
| - name: pronoun | |
| dtype: string | |
| - name: pronoun_loc | |
| dtype: int32 | |
| - name: quote | |
| dtype: string | |
| - name: quote_loc | |
| dtype: int32 | |
| - name: options | |
| sequence: string | |
| - name: label | |
| dtype: | |
| class_label: | |
| names: | |
| '0': '0' | |
| '1': '1' | |
| - name: source | |
| dtype: string | |
| splits: | |
| - name: test | |
| num_bytes: 52281 | |
| num_examples: 285 | |
| download_size: 113235 | |
| dataset_size: 52281 | |
| - config_name: wsc273 | |
| features: | |
| - name: text | |
| dtype: string | |
| - name: pronoun | |
| dtype: string | |
| - name: pronoun_loc | |
| dtype: int32 | |
| - name: quote | |
| dtype: string | |
| - name: quote_loc | |
| dtype: int32 | |
| - name: options | |
| sequence: string | |
| - name: label | |
| dtype: | |
| class_label: | |
| names: | |
| '0': '0' | |
| '1': '1' | |
| - name: source | |
| dtype: string | |
| splits: | |
| - name: test | |
| num_bytes: 49674 | |
| num_examples: 273 | |
| download_size: 113235 | |
| dataset_size: 49674 | |
| # Dataset Card for The Winograd Schema Challenge | |
| ## Table of Contents | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#contributions) | |
| ## Dataset Description | |
| - **Homepage:** https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html | |
| - **Repository:** | |
| - **Paper:** https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.729.9814&rep=rep1&type=pdf | |
| - **Leaderboard:** | |
| - **Point of Contact:** | |
| ### Dataset Summary | |
| A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is | |
| resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its | |
| resolution. The schema takes its name from a well-known example by Terry Winograd: | |
| > The city councilmen refused the demonstrators a permit because they [feared/advocated] violence. | |
| If the word is ``feared'', then ``they'' presumably refers to the city council; if it is ``advocated'' then ``they'' | |
| presumably refers to the demonstrators. | |
| ### Supported Tasks and Leaderboards | |
| From the official webpage: | |
| > A contest, entitled the Winograd Schema Challenge was run once, in 2016. At that time, there was a cash prize | |
| offered for achieving human-level performance in the contest. Since then, the sponsor has withdrawn; therefore NO | |
| CASH PRIZES CAN BE OFFERED OR WILL BE AWARDED FOR ANY KIND OF PERFORMANCE OR ACHIEVEMENT ON THIS CHALLENGE. | |
| ### Languages | |
| The dataset is in English. | |
| [Translation of 12 WSs into Chinese ](https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WSChinese.html)(translated by Wei Xu). | |
| Translations into Japanese, by Soichiro Tanaka, Rafal Rzepka, and Shiho Katajima\ | |
| **Translation changing English names to Japanese **[PDF ](https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/collection_ja.pdf) [HTML](http://arakilab.media.eng.hokudai.ac.jp/~kabura/collection_ja.html)\ | |
| **Translation preserving English names** [PDF ](https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/collection_katakana.pdf) [HTML](http://arakilab.media.eng.hokudai.ac.jp/~kabura/collection_katakana.html) | |
| [Translation into French, ](http://www.llf.cnrs.fr/winograd-fr)by Pascal Amsili and Olga Seminck | |
| [Winograd Schemas in Portuguese](https://sol.sbc.org.br/index.php/eniac/article/view/9334) by Gabriela Melo, Vinicius Imaizumi, and Fábio Cozman. | |
| [Mandarinograd: A Chinese Collection of Winograd Schemas](https://www.aclweb.org/anthology/2020.lrec-1.3) by Timothée Bernard and Ting Han, LREC-2020. | |
| ## Dataset Structure | |
| ### Data Instances | |
| Each instance contains a text passage with a designated pronoun and two possible answers indicating which entity in | |
| the passage the pronoun represents. An example instance looks like the following: | |
| ```python | |
| { | |
| 'label': 0, | |
| 'options': ['The city councilmen', 'The demonstrators'], | |
| 'pronoun': 'they', | |
| 'pronoun_loc': 63, | |
| 'quote': 'they feared violence', | |
| 'quote_loc': 63, | |
| 'source': '(Winograd 1972)', | |
| 'text': 'The city councilmen refused the demonstrators a permit because they feared violence.' | |
| } | |
| ``` | |
| ### Data Fields | |
| - `text` (str): The text sequence | |
| - `options` (list[str]): The two entity options that the pronoun may be referring to | |
| - `label` (int): The index of the correct option in the `options` field | |
| - `pronoun` (str): The pronoun in the sequence to be resolved | |
| - `pronoun_loc` (int): The starting position of the pronoun in the sequence | |
| - `quote` (str): The substr with the key action or context surrounding the pronoun | |
| - `quote_loc` (int): The starting position of the quote in the sequence | |
| - `source` (str): A description of the source who contributed the example | |
| ### Data Splits | |
| Only a test split is included. | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| The Winograd Schema Challenge was proposed as an automated evaluation of an AI system's commonsense linguistic | |
| understanding. From the webpage: | |
| > The strengths of the challenge are that it is clear-cut, in that the answer to each schema is a binary choice; | |
| vivid, in that it is obvious to non-experts that a program that fails to get the right answers clearly has serious | |
| gaps in its understanding; and difficult, in that it is far beyond the current state of the art. | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| This data was manually written by experts such that the schemas are: | |
| - easily disambiguated by the human reader (ideally, so easily that the reader does not even notice that there is an ambiguity); | |
| - not solvable by simple techniques such as selectional restrictions; | |
| - Google-proof; that is, there is no obvious statistical test over text corpora that will reliably disambiguate these correctly. | |
| #### Who are the source language producers? | |
| This dataset has grown over time, and so was produced by a variety of lingustic and AI researchers. See the `source` | |
| field for the source of each instance. | |
| ### Annotations | |
| #### Annotation process | |
| Annotations are produced by the experts who construct the examples. | |
| #### Who are the annotators? | |
| See above. | |
| ### Personal and Sensitive Information | |
| [More Information Needed] | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| [More Information Needed] | |
| ### Discussion of Biases | |
| [More Information Needed] | |
| ### Other Known Limitations | |
| [More Information Needed] | |
| ## Additional Information | |
| ### Dataset Curators | |
| This dataset has grown over time, and so was produced by a variety of lingustic and AI researchers. See the `source` | |
| field for the source of each instance. | |
| ### Licensing Information | |
| This work is licensed under a [Creative Commons Attribution 4.0 International | |
| License](https://creativecommons.org/licenses/by/4.0/). | |
| ### Citation Information | |
| The Winograd Schema Challenge including many of the examples here was proposed by | |
| [Levesque et al 2012](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.729.9814&rep=rep1&type=pdf): | |
| ``` | |
| @inproceedings{levesque2012winograd, | |
| title={The winograd schema challenge}, | |
| author={Levesque, Hector and Davis, Ernest and Morgenstern, Leora}, | |
| booktitle={Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning}, | |
| year={2012}, | |
| organization={Citeseer} | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset. |