Blog
The latest from Google Research
Advances in Semantic Textual Similarity
Thursday, May 17, 2018
Posted by Yinfei Yang, Software Engineer and Chris Tar, Engineering Manager, Google AI
The recent rapid progress of neural network-based natural language understanding research, especially on learning semantic text representations, can enable truly novel products such as
Smart Compose
and
Talk to Books
. It can also help improve performance on a variety of natural language tasks which have limited amounts of training data, such as building strong text classifiers from as few as 100 labeled examples.
Below, we discuss two papers reporting recent progress on semantic representation research at Google, as well as two new models available for download on
TensorFlow Hub
that we hope developers will use to build new and exciting applications.
Semantic Textual Similarity
In “
Learning Semantic Textual Similarity from Conversations
”, we introduce a new way to learn sentence representations for semantic textual similarity. The intuition is that sentences are semantically similar if they have a similar distribution of responses. For example, “How old are you?” and “What is your age?” are both questions about age, which can be answered by similar responses such as “I am 20 years old”. In contrast, while “How are you?” and “How old are you?” contain almost identical words, they have very different meanings and lead to different responses.
Sentences are semantically similar if they can be answered by the same responses. Otherwise, they are semantically different.
In this work, we aim to learn semantic similarity by way of a response classification task: given a conversational input, we wish to classify the correct response from a batch of randomly selected responses. But, the ultimate goal is to learn a model that can return encodings representing a variety of natural language relationships, including similarity and relatedness. By adding another prediction task (In this case, the
SNLI
entailment
dataset) and forcing both through shared encoding layers, we get even better performance on similarity measures such as the
STSBenchmark
(a sentence similarity benchmark) and
CQA task B
(a question/question similarity task). This is because logical entailment is quite different from simple equivalence and provides more signal for learning complex semantic representations.
For a given input, classification is considered a ranking problem against potential candidates.
Universal Sentence Encoder
In “
Universal Sentence Encoder
”, we introduce a model that extends the multitask training described above by adding more tasks, jointly training them with a
skip-thought
-like model that predicts sentences surrounding a given selection of text. However, instead of the encoder-decoder architecture in the original skip-thought model, we make use of an encode-only architecture by way of a shared encoder to drive the prediction tasks. In this way, training time is greatly reduced while preserving the performance on a variety of transfer tasks including sentiment and semantic similarity classification. The aim is to provide a single encoder that can support as wide a variety of applications as possible, including paraphrase detection, relatedness, clustering and custom text classification.
Pairwise semantic similarity comparison via outputs from TensorFlow Hub Universal Sentence Encoder.
As described in our paper, one version of the Universal Sentence Encoder model uses a
deep average network
(DAN) encoder, while a second version uses a more complicated self attended network architecture,
Transformer
.
Multi-task training as described in “
Universal Sentence Encoder
”. A variety of tasks and task structures are joined by shared encoder layers/parameters (grey boxes).
With the more complicated architecture, the model performs better than the simpler DAN model on a variety of sentiment and similarity classification tasks, and for short sentences is only moderately slower. However, compute time for the model using Transformer increases noticeably as sentence length increases, whereas the compute time for the DAN model stays nearly constant as sentence length is increased.
New Models
In addition to the Universal Sentence Encoder
model
described above, we are also sharing two
new
models on
TensorFlow Hub
: the
Universal Sentence Encoder - Large
and
Universal Sentence Encoder - Lite
. These are pretrained Tensorflow models that return a semantic encoding for variable-length text inputs. The encodings can be used for semantic similarity measurement, relatedness, classification, or clustering of natural language text.
The Large model is trained with the
Transformer
encoder described in our second paper. It targets scenarios requiring high precision semantic representations and the best model performance at the cost of speed & size.
The Lite model is trained on a
Sentence Piece
vocabulary instead of words in order to significantly reduce the vocabulary size, which is a major contributor of model size. It targets scenarios where resources like memory and CPU are limited, such as on-device or browser based implementations.
We're excited to share this research, and these models, with the community. We believe that what we're showing here is just the beginning, and that there remain important research problems to be addressed, such as extending the techniques to more languages (the models discussed above currently support English). We also hope to further develop this technology so it can understand text at the paragraph or even document level. In achieving these tasks, it may be possible to make an encoder that is truly “universal”.
Acknowledgements
Daniel Cer, Mario Guajardo-Cespedes, Sheng-Yi Kong, Noah Constant for training the models, Nan Hua, Nicole Limtiaco, Rhomni St. John for transferring tasks, Steve Yuan, Yunhsuan Sung, Brian Strope, Ray Kurzweil for discussion of the model architecture. Special thanks to Sheng-Yi Kong and Noah Constant for training the Lite model.
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
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
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
2021
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
.
