Datasets:
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
- semantic-similarity-scoring
- text-scoring
- multi-input-text-classification
paperswithcode_id: paws
pretty_name: 'PAWS: Paraphrase Adversaries from Word Scrambling'
config_names:
- labeled_final
- labeled_swap
- unlabeled_final
tags:
- paraphrase-identification
dataset_info:
- config_name: labeled_final
features:
- name: id
dtype: int32
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
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'1': '1'
splits:
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num_bytes: 12239938
num_examples: 49401
- name: test
num_bytes: 1987794
num_examples: 8000
- name: validation
num_bytes: 1975862
num_examples: 8000
download_size: 10899391
dataset_size: 16203594
- config_name: labeled_swap
features:
- name: id
dtype: int32
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 7963619
num_examples: 30397
download_size: 5741756
dataset_size: 7963619
- config_name: unlabeled_final
features:
- name: id
dtype: int32
- name: sentence1
dtype: string
- name: sentence2
dtype: string
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dtype:
class_label:
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splits:
- name: train
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- name: validation
num_bytes: 2442165
num_examples: 10000
download_size: 112644285
dataset_size: 160248641
configs:
- config_name: labeled_final
data_files:
- split: train
path: labeled_final/train-*
- split: test
path: labeled_final/test-*
- split: validation
path: labeled_final/validation-*
- config_name: labeled_swap
data_files:
- split: train
path: labeled_swap/train-*
- config_name: unlabeled_final
data_files:
- split: train
path: unlabeled_final/train-*
- split: validation
path: unlabeled_final/validation-*
Dataset Card for PAWS: Paraphrase Adversaries from Word Scrambling
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: PAWS
- Repository: PAWS
- Paper: PAWS: Paraphrase Adversaries from Word Scrambling
- Point of Contact: Yuan Zhang
Dataset Summary
PAWS: Paraphrase Adversaries from Word Scrambling
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. The dataset has two subsets, one based on Wikipedia and the other one based on the Quora Question Pairs (QQP) dataset.
For further details, see the accompanying paper: PAWS: Paraphrase Adversaries from Word Scrambling (https://arxiv.org/abs/1904.01130)
PAWS-QQP is not available due to license of QQP. It must be reconstructed by downloading the original data and then running our scripts to produce the data and attach the labels.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
The text in the dataset is in English.
Dataset Structure
Data Instances
Below are two examples from the dataset:
| Sentence 1 | Sentence 2 | Label | |
|---|---|---|---|
| (1) | Although interchangeable, the body pieces on the 2 cars are not similar. | Although similar, the body parts are not interchangeable on the 2 cars. | 0 |
| (2) | Katz was born in Sweden in 1947 and moved to New York City at the age of 1. | Katz was born in 1947 in Sweden and moved to New York at the age of one. | 1 |
The first pair has different semantic meaning while the second pair is a paraphrase. State-of-the-art models trained on existing datasets have dismal performance on PAWS (<40% accuracy); however, including PAWS training data for these models improves their accuracy to 85% while maintaining performance on existing datasets such as the Quora Question Pairs.
Data Fields
This corpus contains pairs generated from Wikipedia pages, and can be downloaded here:
PAWS-Wiki Labeled (Final): 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.
PAWS-Wiki Labeled (Swap-only): containing pairs that have no back translation counterparts and therefore they are not included in the first set. Nevertheless, they are high-quality pairs with human judgements on both paraphrasing and fluency, and they can be included as an auxiliary training set.
PAWS-Wiki Unlabeled (Final): Pairs in this set have noisy labels without human judgments and can also be used as an auxiliary training set. They are generated from both word swapping and back translation methods.
All files are in the tsv format with four columns:
| Column Name | Data |
|---|---|
| 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.
Data Splits
The number of examples and the proportion of paraphrase (Yes%) pairs are shown below:
| Data | Train | Dev | Test | Yes% |
|---|---|---|---|---|
| Labeled (Final) | 49,401 | 8,000 | 8,000 | 44.2% |
| Labeled (Swap-only) | 30,397 | -- | -- | 9.6% |
| Unlabeled (Final) | 645,652 | 10,000 | -- | 50.0% |
Dataset Creation
Curation Rationale
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.
Source Data
Initial Data Collection and Normalization
Their automatic generation method is based on two ideas. The first swaps words to generate a sentence pair with the same BOW, controlled by a language model. The second uses back translation to generate paraphrases with high BOW overlap but different word order. These two strategies generate high-quality, diverse PAWS pairs, balanced evenly between paraphrases and non-paraphrases.
Who are the source language producers?
Mentioned above.
Annotations
Annotation process
Sentence pairs are presented to five annotators, each of which gives a binary judgment as to whether they are paraphrases or not. They chose binary judgments to make dataset have the same label schema as the QQP corpus. Overall, human agreement is high on both Quora (92.0%) and Wikipedia (94.7%) and each label only takes about 24 seconds. As such, answers are usually straight-forward to human raters.
Who are the annotators?
[More Information Needed]
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
List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.
Licensing Information
The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.
Citation Information
@InProceedings{paws2019naacl,
title = {{PAWS: Paraphrase Adversaries from Word Scrambling}},
author = {Zhang, Yuan and Baldridge, Jason and He, Luheng},
booktitle = {Proc. of NAACL},
year = {2019}
}
Contributions
Thanks to @bhavitvyamalik for adding this dataset.