Blog
The latest news from Google AI
Smart Compose: Using Neural Networks to Help Write Emails
Wednesday, May 16, 2018
Posted by Yonghui Wu, Principal Engineer, Google Brain Team
Last week at
Google I/O
, we introduced
Smart Compose
, a new feature in Gmail that uses machine learning to interactively offer sentence completion suggestions as you type, allowing you to draft emails faster. Building upon technology developed for
Smart Reply
, Smart Compose offers a new way to help you compose messages — whether you are responding to an incoming email or drafting a new one from scratch.
In developing Smart Compose, there were a number of key challenges to face, including:
Latency:
Since Smart Compose provides predictions on a per-keystroke basis, it must respond ideally within 100ms for the user not to notice any delays. Balancing model complexity and inference speed was a critical issue.
Scale:
Gmail is used by more than 1.4 billion diverse users. In order to provide auto completions that are useful for all Gmail users, the model has to have enough modeling capacity so that it is able to make tailored suggestions in subtly different contexts.
Fairness and Privacy:
In developing Smart Compose, we needed to address sources of potential bias in the training process, and had to adhere to the same rigorous user privacy standards as Smart Reply, making sure that our models never expose user’s private information. Furthermore, researchers had no access to emails, which meant they had to develop and train a machine learning system to work on a dataset that they themselves cannot read.
Finding the Right Model
Typical language generation models, such as
ngram
,
neural bag-of-words
(BoW) and
RNN language
(RNN-LM) models, learn to predict the next word conditioned on the prefix word sequence. In an email, however, the words a user has typed in the current email composing session is only one “signal” a model can use to predict the next word. In order to incorporate more context about what the user wants to say, our model is also conditioned on the email subject and the previous email body (if the user is replying to an incoming email).
One approach to include this additional context is to cast the problem as a
sequence-to-sequence
(seq2seq) machine translation task, where the source sequence is the concatenation of the subject and the previous email body (if there is one), and the target sequence is the current email the user is composing. While this approach worked well in terms of prediction quality, it failed to meet our strict latency constraints by orders of magnitude.
To improve on this, we combined a BoW model with an RNN-LM, which is faster than the seq2seq models with only a slight sacrifice to model prediction quality. In this hybrid approach, we encode the subject and previous email by averaging the
word embeddings
in each field. We then join those averaged embeddings, and feed them to the target sequence RNN-LM at every decoding step, as the model diagram below shows.
Smart Compose RNN-LM model architecture. Subject and previous email message are encoded by averaging the word embeddings in each field. The averaged embeddings are then fed to the RNN-LM at each decoding step.
Accelerated Model Training & Serving
Of course, once we decided on this modeling approach we still had to tune various model hyperparameters and train the models over billions of examples, all of which can be very time-intensive. To speed things up, we used a full
TPUv2 Pod
to perform experiments. In doing so, we’re able to train a model to convergence in less than a day.
Even after training our faster hybrid model, our initial version of Smart Compose running on a standard CPU had an average serving latency of hundreds of milliseconds, which is still unacceptable for a feature that is trying to save users' time. Fortunately, TPUs can also be used at inference time to greatly speed up the user experience. By offloading the bulk of the computation onto TPUs, we improved the average latency to tens of milliseconds while also greatly increasing the number of requests that can be served by a single machine.
Fairness and Privacy
Fairness in machine learning
is very important, as language understanding models can reflect human cognitive biases resulting in unwanted word associations and sentence completions. As Caliskan et al. point out in their recent paper “
Semantics derived automatically from language corpora contain human-like biases
”, these associations are deeply entangled in natural language data, which presents a considerable challenge to building any language model. We are actively researching ways to continue to reduce potential biases in our training procedures. Also, since Smart Compose is trained on billions of phrases and sentences, similar to the way spam machine learning models are trained, we have done extensive testing to make sure that only common phrases used by multiple users are memorized by our model, using findings from
this paper
.
Future work
We are constantly working on improving the suggestion quality of the language generation model by following state-of-the-art architectures (e.g.,
Transformer,
RNMT+
, etc.) and experimenting with most recent and advanced training techniques. We will deploy those more advanced models to production once our strict latency constraints can be met. We are also working on incorporating personal language models, designed to more accurately emulate an individual’s style of writing into our system.
Acknowledgements
Smart Compose language generation model was developed by Benjamin Lee, Mia Chen, Gagan Bansal, Justin Lu, Jackie Tsay, Kaushik Roy, Tobias Bosch, Yinan Wang, Matthew Dierker, Katherine Evans, Thomas Jablin, Dehao Chen, Vinu Rajashekhar, Akshay Agrawal, Yuan Cao, Shuyuan Zhang, Xiaobing Liu, Noam Shazeer, Andrew Dai, Zhifeng Chen, Rami Al-Rfou, DK Choe, Yunhsuan Sung, Brian Strope, Timothy Sohn, Yonghui Wu, and many others.
Labels
2018
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
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
.
