Blog
The latest news from Google AI
An NLU-Powered Tool to Explore COVID-19 Scientific Literature
Monday, May 4, 2020
Posted by Keith Hall, Research Scientist, Natural Language Understanding, Google Research
Due to the COVID-19 pandemic, scientists and researchers around the world are publishing an immense amount of new research in order to understand and combat the disease. While the volume of research is very encouraging, it can be difficult for scientists and researchers to keep up with the rapid pace of new publications. Traditional search engines can be excellent resources for finding real-time information on general COVID-19 questions like "How many COVID-19 cases are there in the United States?", but can struggle with understanding the meaning behind
research-driven
queries. Furthermore, searching through the existing corpus of COVID-19 scientific literature with traditional keyword-based approaches can make it difficult to pinpoint relevant evidence for complex queries.
To help address this problem, we are launching the
COVID-19 Research Explorer
, a semantic search interface on top of the
COVID-19 Open Research Dataset
(CORD-19), which includes more than 50,000 journal articles and preprints. We have designed the tool with the goal of helping scientists and researchers efficiently pore through articles for answers or evidence to COVID-19-related questions.
When the user asks an initial question, the tool not only returns a set of papers (like in a traditional search) but also highlights snippets from the paper that are possible answers to the question. The user can review the snippets and quickly make a decision on whether or not that paper is worth further reading. If the user is satisfied with the initial set of papers and snippets, we have added functionality to pose follow-up questions, which act as new queries for the original set of retrieved articles. Take a look at the animation below to see an example of a query and a corresponding follow-up question. We hope these features will foster knowledge exploration and efficient gathering of evidence for scientific hypotheses.
Semantic Search
A key technology powering the tool is
semantic search
. Semantic search aims to not just capture term overlap between a query and a document, but to really understand whether the meaning of a phrase is relevant to the user’s true intent behind their query.
Consider the query, “
What regulates ACE2 expression?
” Even though this seems like a simple question, certain phrases can still confuse a search engine that relies solely on text matching. For example, “
regulates
” can refer to a number of biological processes. While traditional
information retrieval
(IR) systems use techniques like
query expansion
to mitigate this confusion, semantic search models aim to learn these relationships implicitly.
Word order also matters. ACE2 (
angiotensin converting enzyme-2
) itself regulates certain biological processes, but the question is actually asking what regulates ACE2. Matching on terms alone will not distinguish between “what regulates ACE2 ” and “what ACE2 regulates.” Traditional IR systems use tricks like
n-gram
term matching, but semantic search methods strive to model word order and semantics at their core.
The semantic search technology we use is powered by
BERT
, which has
recently been deployed
to improve retrieval quality of
Google Search
. For the COVID-19 Research Explorer we faced the challenge that biomedical literature uses a language that is very different from the kinds of queries submitted to Google.com. In order to train BERT models, we required supervision — examples of queries and their relevant documents and snippets. While we relied on excellent resources produced by
BioASQ
for fine-tuning, such human-curated datasets tend to be small. Neural semantic search models require large amounts of training data. To augment small human-constructed datasets, we used advances in query generation to build a
large synthetic corpus of questions and relevant documents
in the biomedical domain.
Specifically, we used large amounts of general domain question-answer pairs to train an
encoder-decoder model
(part a in the figure below). This kind of neural architecture is used in tasks like
machine translation
that encodes one piece of text (e.g., an English sentence) and produces another piece of text (e.g., a French sentence). Here we trained the model to translate from answer passages to questions (or queries) about that passage. Next we took passages from every document in the collection, in this case CORD-19, and generated corresponding queries (part b). We then used these synthetic query-passage pairs as supervision to train our neural retrieval model (part c).
Synthetic query construction.
However, we found that there were examples where the neural model performed worse than a keyword-based model. This is because of the
memorization-generalization continuum
, which is well known in most fields of artificial intelligence and psycholinguistics. Keyword-based models, like
tf-idf
, are essentially memorizers. They memorize terms from the query and look for documents that have them. Neural retrieval models, on the other hand, learn generalizations about concepts and meaning and try to match based on those. Sometimes they can over-generalize when precision is important. For example, if I query, “What regulates ACE2 expression?”, one may want the model to generalize the concept of “regulation,” but not ACE2 beyond acronym expansion.
Hybrid Term-Neural Retrieval Model
To improve our system we built a hybrid term-neural retrieval model. A crucial observation is that both term-based and neural models can be cast as a
vector space model
. In other words, we can encode both the query and documents and then treat retrieval as looking for the document vectors that are most similar to the query vector, also known as
k-nearest neighbor
retrieval. There is a
lot of research and engineering
that is needed to make this work at scale, but it allows us a simple mechanism to combine methods. The simplest approach is to combine the vectors with a trade-off parameter.
Hybrid Term and Neural Retrieval.
In the figure above, the blue boxes are the term-based vectors, and the red, the neural vectors. We represent documents by concatenating these vectors. We concatenate the two vectors for queries as well, but we control the relative importance of exact term matches versus neural semantic matching. This is done via a weight parameter
k
. While more complex hybrid schemes are possible, we found that this simple hybrid model significantly increased quality on our biomedical literature retrieval benchmarks.
Availability and Community Feedback
The COVID-19 Research Explorer is freely available to the research community as an open alpha. Over the coming months we will be making a number of usability enhancements, so please check back often. Try out the
COVID-19 Research Explorer
, and please share any comments you have with us via the feedback channels on the site.
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): John Alex, Waleed Ammar, Greg Billock, Yale Cong, Ali Elkahky, Daniel Francisco, Stephen Greco, Stefan Hosein, Johanna Katz, Gyorgy Kiss, Margarita Kopniczky, Ivan Korotkov, Dominic Leung, Daphne Luong, Ji Ma, Ryan Mcdonald, Matt Pearson-Beck, Biao She, Jonathan Sheffi, Kester Tong, Ben Wedin
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
.
