paws_wiki

• Description:

Existing paraphrase identification datasets lack sentence pairs that have high lexical overlap without being paraphrases. Models trained on such data fail to distinguish pairs like flights from New York to Florida and flights from Florida to New York. This dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature the importance of modeling structure, context, and word order information for the problem of paraphrase identification.

For further details, see the accompanying paper: PAWS: Paraphrase Adversaries from Word Scrambling at https://arxiv.org/abs/1904.01130

This corpus contains pairs generated from Wikipedia pages, containing pairs that are generated from both word swapping and back translation methods. All pairs have human judgements on both paraphrasing and fluency and they are split into Train/Dev/Test sections.

All files are in the tsv format with four columns:

id A unique id for each pair sentence1 The first sentence sentence2 The second sentence (noisy_)label (Noisy) label for each pair

Each label has two possible values: 0 indicates the pair has different meaning, while 1 indicates the pair is a paraphrase.

• Homepage: https://github.com/google-research-datasets/paws

• Source code: tfds.text.PawsWiki

• Versions:

  • 1.0.0 (default): No release notes.

• Download size: 4.47 MiB

• Dataset size: 17.96 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'test'	8,000
'train'	49,401
'validation'	8,000

• Features:

FeaturesDict({
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    'sentence1': Text(shape=(), dtype=tf.string),
    'sentence2': Text(shape=(), dtype=tf.string),
})

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@InProceedings{paws2019naacl,
  title = { {PAWS: Paraphrase Adversaries from Word Scrambling} },
  author = {Zhang, Yuan and Baldridge, Jason and He, Luheng},
  booktitle = {Proc. of NAACL},
  year = {2019}
}