- Description:
GAP is a gender-balanced dataset containing 8,908 coreference-labeled pairs of (ambiguous pronoun, antecedent name), sampled from Wikipedia and released by Google AI Language for the evaluation of coreference resolution in practical applications.
- Additional Documentation: Explore on Papers With Code 
- Homepage: https://github.com/google-research-datasets/gap-coreference 
- Source code: - tfds.text.Gap
- Versions: - 0.1.0: Initial release.
- 0.1.1(default): Fixes parsing of boolean field- A-corefand- B-coref.
 
- Download size: - 2.29 MiB
- Dataset size: - 2.96 MiB
- Auto-cached (documentation): Yes 
- Splits: 
| Split | Examples | 
|---|---|
| 'test' | 2,000 | 
| 'train' | 2,000 | 
| 'validation' | 454 | 
- Feature structure:
FeaturesDict({
    'A': Text(shape=(), dtype=string),
    'A-coref': bool,
    'A-offset': int32,
    'B': Text(shape=(), dtype=string),
    'B-coref': bool,
    'B-offset': int32,
    'ID': Text(shape=(), dtype=string),
    'Pronoun': Text(shape=(), dtype=string),
    'Pronoun-offset': int32,
    'Text': Text(shape=(), dtype=string),
    'URL': Text(shape=(), dtype=string),
})
- Feature documentation:
| Feature | Class | Shape | Dtype | Description | 
|---|---|---|---|---|
| FeaturesDict | ||||
| A | Text | string | ||
| A-coref | Tensor | bool | ||
| A-offset | Tensor | int32 | ||
| B | Text | string | ||
| B-coref | Tensor | bool | ||
| B-offset | Tensor | int32 | ||
| ID | Text | string | ||
| Pronoun | Text | string | ||
| Pronoun-offset | Tensor | int32 | ||
| Text | Text | string | ||
| URL | Text | string | 
- Supervised keys (See - as_superviseddoc):- None
- Figure (tfds.show_examples): Not supported. 
- Examples (tfds.as_dataframe): 
- Citation:
@article{DBLP:journals/corr/abs-1810-05201,
  author    = {Kellie Webster and
               Marta Recasens and
               Vera Axelrod and
               Jason Baldridge},
  title     = {Mind the {GAP:} {A} Balanced Corpus of Gendered Ambiguous Pronouns},
  journal   = {CoRR},
  volume    = {abs/1810.05201},
  year      = {2018},
  url       = {http://arxiv.org/abs/1810.05201},
  archivePrefix = {arXiv},
  eprint    = {1810.05201},
  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1810-05201},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}