Blog
The latest news from Google AI
A Scalable Approach to Reducing Gender Bias in Google Translate
Wednesday, April 22, 2020
Posted by Melvin Johnson, Senior Software Engineer, Google Research
Machine learning (ML) models for language translation can be skewed by societal biases reflected in their training data. One such example, gender bias, often becomes more apparent when translating between a gender-specific language and one that is less-so. For instance, Google Translate historically translated the Turkish equivalent of “
He/she is a doctor
” into the masculine form, and the Turkish equivalent of “
He/she is a nurse
” into the feminine form.
In line with Google’s
AI Principles
, which emphasizes the importance to avoid creating or reinforcing unfair biases, in December 2018 we
announced
gender-specific translations. This feature in Google Translate provides options for both feminine and masculine translations when translating queries that are gender-neutral in the source language. For this work, we developed a
three-step approach
, which involved detecting gender-neutral queries, generating gender-specific translations and checking for accuracy. We used this approach to enable gender-specific translations for phrases and sentences in Turkish-to-English and have now expanded this approach for English-to-Spanish translations, the most popular language-pair in Google Translate.
Left:
Early example of the translation of a gender neutral English phrase to a gender-specific Spanish counterpart. In this case, only a biased example is given.
Right:
The new Translate provides both a feminine and a masculine translation option.
But as this approach was applied to more languages, it became apparent that there were issues in scaling. Specifically, generating masculine and feminine translations independently using a
neural machine translation
(NMT) system resulted in low
recall
, failing to show gender-specific translations for up to 40% of eligible queries, because the two translations often weren’t exactly equivalent, except for gender-related phenomena. Additionally, building a classifier to detect gender-neutrality for each source language was data intensive.
Today, along with the release of the new English-to-Spanish gender-specific translations, we announce an improved approach that uses a dramatically different paradigm to address gender bias by rewriting or post-editing the initial translation. This approach is more scalable, especially when translating from gender-neutral languages to English, since it does not require a gender-neutrality detector. Using this approach we have expanded gender-specific translations to include Finnish, Hungarian, and Persian-to-English. We have also replaced the previous Turkish-to-English system using the new rewriting-based method.
Rewriting-Based Gender-Specific Translation
The first step in the rewriting-based method is to generate the initial translation. The translation is then reviewed to identify instances where a gender-neutral source phrase yielded a gender-specific translation. If that is the case, we apply a sentence-level rewriter to generate an alternative gendered translation. Finally, both the initial and the rewritten translations are reviewed to ensure that the only difference is the gender.
Top:
The original approach.
Bottom:
The new rewriting-based approach.
Rewriter
Building a rewriter involved generating millions of training examples composed of pairs of phrases, each of which included both masculine and feminine translations. Because such data was not readily available, we generated a new dataset for this purpose. Starting with a large monolingual dataset, we programmatically generated candidate rewrites by swapping gendered pronouns from masculine to feminine, or vice versa. Since there can be multiple valid candidates, depending on the context — for example the feminine pronoun “her” can map to either “him” or “his” and the masculine pronoun “his” can map to “her” or “hers” — a mechanism was needed for choosing the correct one. To resolve this tie, one can either use a
syntactic parser
or a
language model
. Because a syntactic parsing model would require training with labeled datasets in each language, it is less scalable than a language model, which can learn in an unsupervised fashion. So, we select the best candidate using an in-house language model trained on millions of English sentences.
This table demonstrates the data generation process. We start with the input, generate candidates and finally break the tie using a language model.
The above data generation process results in training data that goes from a masculine input to a feminine output and vice versa. We merge data from both these directions and train a one-layer
transformer-based
sequence-to-sequence
model on it. We introduce punctuation and casing variants in the training data to increase the model robustness. Our final model can reliably produce the requested masculine or feminine rewrites 99% of the time.
Evaluation
We also devised a new method of evaluation, named
bias reduction,
which measures the relative reduction of bias between the new translation system and the existing system. Here “bias” is defined as making a gender choice in the translation that is unspecified in the source. For example, if the current system is biased 90% of the time and the new system is biased 45% of the time, this results in a 50% relative bias reduction. Using this metric, the new approach results in a bias reduction of ≥90% for translations from Hungarian, Finnish and Persian-to-English. The bias reduction of the existing Turkish-to-English system improved from 60% to 95% with the new approach. Our system triggers gender-specific translations with an average precision of 97% (i.e., when we decide to show gender-specific translations we’re right 97% of the time).
We’ve made significant progress since our initial launch by increasing the quality of gender-specific translations and also expanding it to 4 more language-pairs. We are committed to further addressing gender bias in Google Translate and plan to extend this work to document-level translation, as well.
Acknowledgements:
This effort has been successful thanks to the hard work of many people, including, but not limited to the following (in alphabetical order of last name): Anja Austermann, Jennifer Choi,
Hossein Emami,
Rick Genter,
Megan Hancock,
Mikio Hirabayashi, Macduff Hughes, Tolga Kayadelen,
Mira Keskinen,
Michelle Linch, Klaus Macherey,
Gergely Morvay,
Tetsuji Nakagawa, Thom Nelson, Mengmeng Niu, Jennimaria Palomaki, Alex Rudnick, Apu Shah, Jason Smith, Romina Stella, Vilmos Urban, Colin Young, Angie Whitnah, Pendar Yousefi, Tao Yu
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
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
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
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
2020
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
.
