wsc273
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WSC273 is a common sense reasoning benchmark that requires the system to read a
sentence with an ambiguous pronoun and select the referent of that pronoun from
two choices. It contains the first 273 examples from the Winograd Schema
Challenge. 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.
Split |
Examples |
'test' |
273 |
FeaturesDict({
'idx': int32,
'label': int32,
'option1': Text(shape=(), dtype=string),
'option1_normalized': Text(shape=(), dtype=string),
'option2': Text(shape=(), dtype=string),
'option2_normalized': Text(shape=(), dtype=string),
'pronoun_end': int32,
'pronoun_start': int32,
'pronoun_text': Text(shape=(), dtype=string),
'text': Text(shape=(), dtype=string),
})
Feature |
Class |
Shape |
Dtype |
Description |
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FeaturesDict |
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idx |
Tensor |
|
int32 |
|
label |
Tensor |
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int32 |
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option1 |
Text |
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string |
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option1_normalized |
Text |
|
string |
|
option2 |
Text |
|
string |
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option2_normalized |
Text |
|
string |
|
pronoun_end |
Tensor |
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int32 |
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pronoun_start |
Tensor |
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int32 |
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pronoun_text |
Text |
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string |
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text |
Text |
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string |
|
@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}
}
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Last updated 2022-12-06 UTC.
[null,null,["Last updated 2022-12-06 UTC."],[],[],null,["# wsc273\n\n\u003cbr /\u003e\n\n- **Description**:\n\nWSC273 is a common sense reasoning benchmark that requires the system to read a\nsentence with an ambiguous pronoun and select the referent of that pronoun from\ntwo choices. It contains the first 273 examples from the Winograd Schema\nChallenge. A Winograd schema is a pair of sentences that differ in only one or\ntwo words and that contain an ambiguity that is resolved in opposite ways in the\ntwo sentences and requires the use of world knowledge and reasoning for its\nresolution. The schema takes its name from a well-known example by Terry\nWinograd: `The city councilmen refused the demonstrators a permit because they\n[feared/advocated] violence.'' If the word is`feared'', then `they'' presumably\nrefers to the city council; if it is`advocated'' then \\`\\`they'' presumably refers\nto the demonstrators.\n\n- **Additional Documentation** :\n [Explore on Papers With Code\n north_east](https://paperswithcode.com/dataset/wsc)\n\n- **Homepage** :\n \u003chttps://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html\u003e\n\n- **Source code** :\n [`tfds.text.wsc273.Wsc273`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/text/wsc273/wsc273.py)\n\n- **Versions**:\n\n - **`1.0.0`** (default): No release notes.\n- **Download size** : `110.58 KiB`\n\n- **Dataset size** : `87.15 KiB`\n\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n Yes\n\n- **Splits**:\n\n| Split | Examples |\n|----------|----------|\n| `'test'` | 273 |\n\n- **Feature structure**:\n\n FeaturesDict({\n 'idx': int32,\n 'label': int32,\n 'option1': Text(shape=(), dtype=string),\n 'option1_normalized': Text(shape=(), dtype=string),\n 'option2': Text(shape=(), dtype=string),\n 'option2_normalized': Text(shape=(), dtype=string),\n 'pronoun_end': int32,\n 'pronoun_start': int32,\n 'pronoun_text': Text(shape=(), dtype=string),\n 'text': Text(shape=(), dtype=string),\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|--------------------|--------------|-------|--------|-------------|\n| | FeaturesDict | | | |\n| idx | Tensor | | int32 | |\n| label | Tensor | | int32 | |\n| option1 | Text | | string | |\n| option1_normalized | Text | | string | |\n| option2 | Text | | string | |\n| option2_normalized | Text | | string | |\n| pronoun_end | Tensor | | int32 | |\n| pronoun_start | Tensor | | int32 | |\n| pronoun_text | Text | | string | |\n| text | Text | | string | |\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `None`\n\n- **Figure**\n ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):\n Not supported.\n\n- **Examples**\n ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\n- **Citation**:\n\n @inproceedings{levesque2012winograd,\n title={The winograd schema challenge},\n author={Levesque, Hector and Davis, Ernest and Morgenstern, Leora},\n booktitle={Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning},\n year={2012},\n organization={Citeseer}\n }"]]