Blog
The latest news from Google AI
ALBERT: A Lite BERT for Self-Supervised Learning of Language Representations
Friday, December 20, 2019
Posted by Radu Soricut and Zhenzhong Lan, Research Scientists, Google Research
Ever since the advent of
BERT
a year ago, natural language research has embraced a new paradigm, leveraging large amounts of existing text to pretrain a model’s parameters using self-supervision, with no data annotation required. So, rather than needing to train a machine-learning model for
natural language processing
(NLP) from scratch, one can start from a model primed with knowledge of a language. But, in order to improve upon this new approach to NLP, one must develop an understanding of what, exactly, is contributing to language-understanding performance — the network’s height (i.e., number of layers), its width (size of the hidden layer representations), the learning criteria for self-supervision, or something else entirely?
In “
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
”, accepted at
ICLR 2020
, we present an upgrade to BERT that advances the state-of-the-art performance on 12 NLP tasks, including the competitive
Stanford Question Answering Dataset
(SQuAD v2.0) and the
SAT
-style reading comprehension
RACE
benchmark. ALBERT is being
released
as an open-source implementation on top of
TensorFlow
, and includes a number of ready-to-use ALBERT pre-trained language representation models.
What Contributes to NLP Performance?
Identifying the dominant driver of NLP performance is complex — some settings are more important than others, and, as our study reveals, a simple, one-at-a-time exploration of these settings would not yield the correct answers.
The key to optimizing performance, captured in the design of ALBERT, is to allocate the model’s capacity more efficiently. Input-level embeddings (words, sub-tokens, etc.) need to learn context-
independent
representations, a representation for the word “bank”, for example. In contrast, hidden-layer embeddings need to refine that into context-
dependent
representations, e.g., a representation for “bank” in the context of financial transactions, and a different representation for “bank” in the context of river-flow management.
This is achieved by factorization of the embedding parametrization — the embedding matrix is split between input-level embeddings with a relatively-low dimension (e.g., 128), while the hidden-layer embeddings use higher dimensionalities (768 as in the BERT case, or more). With this step alone, ALBERT achieves an 80% reduction in the parameters of the projection block, at the expense of only a minor drop in performance — 80.3
SQuAD2.0
score, down from 80.4; or 67.9 on
RACE
, down from 68.2 — with all other conditions the same as for BERT.
Another critical design decision for ALBERT stems from a different observation that examines redundancy.
Transformer
-based neural network architectures (such as
BERT
,
XLNet
, and
RoBERTa
) rely on independent layers stacked on top of each other. However, we observed that the network often learned to perform similar operations at various layers, using different parameters of the network. This possible redundancy is eliminated in ALBERT by parameter-sharing across the layers, i.e., the same layer is applied on top of each other. This approach slightly diminishes the accuracy, but the more compact size is well worth the tradeoff. Parameter sharing achieves a 90% parameter reduction for the
attention-feedforward
block (a 70% reduction overall), which, when applied in addition to the factorization of the embedding parameterization, incur a slight performance drop of -0.3 on
SQuAD2.0
to 80.0, and a larger drop of -3.9 on
RACE
score to 64.0.
Implementing these two design changes together yields an ALBERT-base model that has only 12M parameters, an 89% parameter reduction compared to the BERT-base model, yet still achieves respectable performance across the benchmarks considered. But this parameter-size reduction provides the opportunity to scale up the model again. Assuming that memory size allows, one can scale up the size of the hidden-layer embeddings by 10-20x. With a hidden-size of 4096, the ALBERT-xxlarge configuration achieves both an overall 30% parameter reduction compared to the BERT-large model, and, more importantly, significant performance gains: +4.2 on
SQuAD2.0
(88.1, up from 83.9), and +8.5 on
RACE
(82.3, up from 73.8).
These results indicate that accurate language understanding depends on developing robust, high-capacity contextual representations. The context, modeled in the hidden-layer embeddings, captures the meaning of the words, which in turn drives the overall understanding, as directly measured by model performance on standard benchmarks.
Optimized Model Performance with the RACE Dataset
To evaluate the language understanding capability of a model, one can administer a reading comprehension test (e.g., similar to the
SAT Reading Test
). This can be done with the
RACE dataset
(2017), the largest publicly available resource for this purpose. Computer performance on this reading comprehension challenge mirrors well the language modeling advances of the last few years: a model pre-trained with only context-independent word representations scores poorly on this test (45.9; left-most bar), while BERT, with context-dependent language knowledge, scores relatively well with a 72.0. Refined BERT models, such as
XLNet
and
RoBERTa
, set the bar even higher, in the 82-83 score range. The ALBERT-xxlarge configuration mentioned above yields a RACE score in the same range (82.3), when trained on the base BERT dataset (Wikipedia and Books). However, when trained on the same larger dataset as XLNet and RoBERTa, it significantly outperforms all other approaches to date, and establishes a new state-of-the-art score at 89.4.
Machine performance on the RACE challenge (SAT-like reading comprehension). A random-guess baseline score is 25.0. The maximum possible score is 95.0.
The success of ALBERT demonstrates the importance of identifying the aspects of a model that give rise to powerful contextual representations. By focusing improvement efforts on these aspects of the model architecture, it is possible to greatly improve both the model efficiency and performance on a wide range of NLP tasks. To facilitate further advances in the field of NLP, we are
open-sourcing ALBERT
to the research community.
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
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
.
