Blog
The latest from Google Research
Releasing PAWS and PAWS-X: Two New Datasets to Improve Natural Language Understanding Models
Wednesday, October 2, 2019
Posted by Yuan Zhang, Research Scientist and Yinfei Yang, Software Engineer, Google Research
Word order and syntactic structure have a large impact on sentence meaning — even small perturbations in word order can completely change interpretation. For example, consider the following related sentences:
Flights from New York to Florida.
Flights to Florida from New York.
Flights from Florida to New York.
All three have the same set of words. However, 1 and 2 have the same meaning — known as
paraphrase pairs
— while 1 and 3 have very different meanings — known as
non-paraphrase pairs
. The task of identifying whether pairs are paraphrase or not is called paraphrase identification, and this task is important to many real-world natural language understanding (NLU) applications such as question answering. Perhaps surprisingly, even state-of-the-art models, like
BERT
, would fail to correctly identify the difference between many non-paraphrase pairs like 1 and 3 above if trained only on existing NLU datasets. This is because existing datasets lack training pairs like this, so it is hard for machine learning models to learn this pattern even if they have the capability to understand complex contextual phrasings.
To address this, we are releasing two new datasets for use in the research community:
Paraphrase Adversaries from Word Scrambling
(PAWS) in English, and
PAWS-X
, an extension of the PAWS dataset to six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. Both datasets contain well-formed sentence pairs with high
lexical overlap
, in which about half of the pairs are paraphrase and others are not. Including new pairs in training data for state-of-the-art models improves their accuracy on this problem from <50% to 85-90%. In contrast, models that do not capture non-local contextual information fail even with new training examples. The new datasets therefore provide an effective instrument for measuring the sensitivity of models to word order and structure.
The PAWS dataset contains 108,463 human-labeled pairs in English, sourced from
Quora Question Pairs
(QQP) and
Wikipedia
pages. PAWS-X contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs. The table below gives detailed statistics of the datasets.
PAWS
PAWS-X
Language
English
English
Chinese
French
German
Japanese
Korean
Spanish
(QQP)
(Wiki)
(Wiki)
(Wiki)
(Wiki)
(Wiki)
(Wiki)
(Wiki)
Training
11,988
79,798
49,401
†
49,401
†
49,401
†
49,401
†
49,401
†
49,401
†
Dev
677
8,000
1,984
1,992
1,932
1,980
1,965
1,962
Test
-
8,000
1,975
1,985
1,967
1,946
1,972
1,999
† The training set of PAWS-X is machine translated from a subset of the PAWS Wiki dataset in English.
Creating the PAWS Dataset in English
In “
PAWS: Paraphrase Adversaries from Word Scrambling
,” we introduce a workflow for generating pairs of sentences that have high word overlap, but which are balanced with respect to whether they are paraphrases or not. To generate examples, source sentences are first passed to a specialized
language model
that creates word-swapped variants that are still semantically meaningful, but ambiguous as to whether they are paraphrase pairs or not. These were then judged by human raters for grammaticality and then multiple raters judged whether they were paraphrases of each other.
PAWS corpus creation workflow.
One problem with this swapping strategy is that it tends to produce pairs that aren't paraphrases (e.g., "
why do bad things happen to good people
" != "
why do good things happen to bad people
"). In order to ensure balance between paraphrases and non-paraphrases, we added other examples based on
back-translation
. Back-translation has the opposite bias as it tends to preserve meaning while changing word order and word choice. These two strategies lead to PAWS being balanced overall, especially for the Wikipedia portion.
Creating the Multilingual PAWS-X Dataset
After creating PAWS,
we extended it
to six more languages: Chinese, French, German, Korean, Japanese, and Spanish. We hired human translators to translate the development and test sets, and used a
neural machine translation (NMT) service
to translate the training set.
We obtained human translations (native speakers) on a random sample of 4,000 sentence pairs from the PAWS development set for each of the six languages (48,000 translations). Each sentence in a pair is presented independently so that translation is not affected by context. A randomly sampled subset was validated by a second worker. The final dataset has less than 5% word level error rate.
Note, we allowed professionals to not translate a sentence if it was incomplete or ambiguous. On average, less than 2% of the pairs were not translated, and we simply excluded them. The final translated pairs are split then into new development and test sets, ~2,000 pairs for each.
Examples of human translated pairs for German(de) and Chinese(zh).
Language Understanding with PAWS and PAWS-X
We train multiple models on the created dataset and measure the classification accuracy on the eval set. When trained with PAWS, strong models, such as
BERT
and
DIIN
, show remarkable improvement over when they are trained on the existing
Quora Question Pairs
(QQP) dataset. For example, on the PAWS data sourced from QQP (PAWS-QQP), BERT gets only 33.5 accuracy if trained on existing QQP, but it recovers to 83.1 accuracy when given PAWS training examples. Unlike BERT, a simple
Bag-of-Words
(BOW) model fails to learn from PAWS training examples, demonstrating its weakness at capturing non-local contextual information. These results demonstrate that PAWS effectively measures sensitivity of models to word order and structure.
Accuracy on PAWS-QQP Eval Set (English).
The figure below shows the performance of the popular
multilingual BERT
model on PAWS-X using several common strategies:
Zero Shot
: The model is trained on the PAWS English training data, and then directly evaluated on all others. Machine translation is
not
involved in this strategy.
Translate Test
: Train a model using the English training data, and machine-translate all test examples to English for evaluation.
Translate Train
: The English training data is machine-translated into each target language to provide data to train each model.
Merged
: Train a multilingual model on all languages, including the original English pairs and machine-translated data in all other languages.
The results show that cross-lingual techniques help, while it also leaves considerable headroom to drive multilingual research on the problem of paraphrase identification
Accuracy of PAWS-X Test Set using BERT Models.
It is our hope that these datasets will be useful to the research community to drive further progress on multilingual models that better exploit structure, context, and pairwise comparisons.
Acknowledgements
The core team includes Luheng He, Jason Baldridge, Chris Tar. We would like to thank the Language team in Google Research, especially Emily Pitler, for the insightful comments that contributed to our papers. Many thanks also to Ashwin Kakarla, Henry Jicha, and Mengmeng Niu, for the help with the annotations.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
expla
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
materials science
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Responsible AI
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2022
Jan
2021
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2020
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
