Blog
The latest news from Google AI
Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing
Friday, November 2, 2018
Posted by Jacob Devlin and Ming-Wei Chang, Research Scientists, Google AI Language
One of the biggest challenges in
natural language processing
(NLP) is the shortage of training data. Because NLP is a diversified field with many distinct tasks, most task-specific datasets contain only a few thousand or a few hundred thousand human-labeled training examples. However, modern deep learning-based NLP models see benefits from much larger amounts of data, improving when trained on millions, or
billions
, of annotated training examples. To help close this gap in data, researchers have developed a variety of techniques for training general purpose language representation models using the enormous amount of unannotated text on the web (known as
pre-training
). The pre-trained model can then be
fine-tuned
on small-data NLP tasks like
question answering
and
sentiment analysis
, resulting in substantial accuracy improvements compared to training on these datasets from scratch.
This week, we
open sourced
a new technique for NLP pre-training called
B
idirectional
E
ncoder
R
epresentations from
T
ransformers
, or
BERT
. With this release, anyone in the world can train their own state-of-the-art question answering system (or a variety of other models) in about 30 minutes on a single
Cloud TPU
, or in a few hours using a single GPU. The release includes source code built on top of
TensorFlow
and a number of pre-trained language representation models.
In our associated paper
, we demonstrate state-of-the-art results on 11 NLP tasks, including the very competitive
Stanford Question Answering Dataset
(SQuAD v1.1).
What Makes BERT Different?
BERT builds upon recent work in pre-training contextual representations — including
Semi-supervised Sequence Learning
,
Generative Pre-Training
,
ELMo
, and
ULMFit
. However, unlike these previous models, BERT is the first
deeply bidirectional
,
unsupervised
language representation, pre-trained using only a plain text corpus (in this case,
Wikipedia
).
Why does this matter? Pre-trained representations can either be
context-free
or
contextual
, and
contextual
representations can further be
unidirectional
or
bidirectional
. Context-free models such as
word2vec
or
GloVe
generate a single
word embedding
representation for each word in the vocabulary. For example, the word “
bank
” would have the same context-free representation in “
bank account
” and “
bank of the river.
” Contextual models instead generate a representation of each word that is based on the other words in the sentence. For example, in the sentence “
I accessed the bank account
,” a unidirectional contextual model would represent “
bank
” based on “
I accessed the
” but not “
account
.” However, BERT represents “
bank
” using both its previous and next context — “
I accessed the
...
account
” — starting from the very bottom of a deep neural network, making it deeply bidirectional.
A visualization of BERT’s neural network architecture compared to previous state-of-the-art contextual pre-training methods is shown below. The arrows indicate the information flow from one layer to the next. The green boxes at the top indicate the final contextualized representation of each input word:
BERT is deeply bidirectional, OpenAI GPT is unidirectional, and ELMo is shallowly bidirectional.
The Strength of Bidirectionality
If bidirectionality is so powerful, why hasn’t it been done before? To understand why, consider that unidirectional models are efficiently trained by predicting each word conditioned on the previous words in the sentence. However, it is not possible to train bidirectional models by simply conditioning each word on its previous
and
next words, since this would allow the word that’s being predicted to indirectly “see itself” in a multi-layer model.
To solve this problem, we use the straightforward technique of masking out some of the words in the input and then condition each word bidirectionally to predict the masked words. For example:
While this idea has been around for a
very long time
, BERT is the first time it was successfully used to pre-train a deep neural network.
BERT also learns to model relationships between sentences by pre-training on a very simple task that can be generated from any text corpus: Given two sentences
A
and
B
, is
B
the actual next sentence that comes after
A
in the corpus, or just a random sentence? For example:
Training with Cloud TPUs
Everything that we’ve described so far might seem fairly straightforward, so what’s the missing piece that made it work so well?
Cloud TPUs
. Cloud TPUs gave us the freedom to quickly experiment, debug, and tweak our models, which was critical in allowing us to move beyond existing pre-training techniques. The
Transformer model architecture
, developed by researchers at Google in 2017, also gave us the foundation we needed to make BERT successful. The Transformer is implemented in our
open source release
, as well as the
tensor2tensor library
.
Results with BERT
To evaluate performance, we compared BERT to other state-of-the-art NLP systems. Importantly, BERT achieved all of its results with almost no task-specific changes to the neural network architecture. On
SQuAD v1.1
, BERT achieves 93.2% F1 score (a measure of accuracy), surpassing the previous state-of-the-art score of 91.6% and human-level score of 91.2%:
BERT also improves the state-of-the-art by 7.6% absolute on the very challenging
GLUE benchmark
, a set of 9 diverse Natural Language Understanding (NLU) tasks. The amount of human-labeled training data in these tasks ranges from 2,500 examples to 400,000 examples, and BERT substantially
improves upon the state-of-the-art
accuracy on all of them:
Making BERT Work for You
The models that we are releasing can be fine-tuned on a wide variety of NLP tasks in a few hours or less. The open source release also includes code to run pre-training, although we believe the majority of NLP researchers who use BERT will never need to pre-train their own models from scratch. The BERT models that we are releasing today are English-only, but we hope to release models which have been pre-trained on a variety of languages in the near future.
The open source TensorFlow implementation and pointers to pre-trained BERT models can be found at
http://goo.gl/language/bert
. Alternatively, you can get started using BERT through
Colab
with the notebook “
BERT FineTuning with Cloud TPUs
.”
You can also read our paper "
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
" for more details.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
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
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
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
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
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
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
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
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
YouTube
Archive
2018
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
Google
on
Follow @googleai
Give us feedback in our
Product Forums
.
