- Description:
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.
Additional Documentation: Explore on Papers With Code
Homepage: https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html
Source code:
tfds.text.wsc273.Wsc273
Versions:
1.0.0
(default): No release notes.
Download size:
110.58 KiB
Dataset size:
87.15 KiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
273 |
- Feature structure:
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 documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
idx | Tensor | int32 | ||
label | Tensor | int32 | ||
option1 | Text | string | ||
option1_normalized | Text | string | ||
option2 | Text | string | ||
option2_normalized | Text | string | ||
pronoun_end | Tensor | int32 | ||
pronoun_start | Tensor | int32 | ||
pronoun_text | Text | string | ||
text | Text | string |
Supervised keys (See
as_supervised
doc):None
Figure (tfds.show_examples): Not supported.
Examples (tfds.as_dataframe):
- Citation:
@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}
}