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AI2 NEWSLETTER |
February 2021
In this
edition: Teaching
AI agents by
playing
hide-and-seek,
TLDRs for
academic papers,
unveiling the
hidden biases in
question
answering models
with cleverly
worded
questions,
revisiting the
ultimate
trajectory of
rejected papers,
and more.
Instead of training the standard
way with huge static collections
of manually labeled images, we
introduce AI agents that can
learn visual representations by
playing
Cache, a variant of hide-and-seek,
where one AI agent hides objects
for another agent to find. We
show that these representations
can achieve surprisingly high
performance, rivaling approaches
developed using traditional
datasets. (More in
this article on the AI2 Blog from lead
author Luca Weihs.)
Semantic Scholar recently launched a new
feature that puts automatically
generated single-sentence paper
summaries right on the
search results page, allowing
you to quickly locate the right
papers and spend your time
reading what matters to you.
TLDRs (Too Long; Didn't
Read) are super-short
summaries of the main objective
and results of a scientific
paper generated using expert
background knowledge and the
latest GPT-3 style NLP
techniques. This new feature is
available in beta for nearly 10
million papers and counting in
the computer science domain in
Semantic Scholar.
"UnQovering" Stereotypical
Biases in Question Answering
Models
The web text used to train
modern NLP systems is more or
less unfiltered, and it contains
all of the stereotypical
associations and implicit biases
one might expect to find in any
huge and aimless collection of
human writing.
In new work that recently
appeared in
Findings of EMNLP 2020,
we ask the question: To what extent are
stereotypical biases present
in question-answering
models?
Read our
blog post
to learn more, and check out the
UnQover demo
to experience it for yourself.
What Open Data Tells Us
About Rejected ICLR
Submissions
Senior Research
Engineer Mark Neumann
writes:
"Having published a few papers
in NLP venues such as ACL, I’ve
often found myself frustrated
with the reviewing process. It
sometimes turns out to be noisy,
with low-quality reviews, which
is an unsatisfying result for
what can often be a lot of hard
work put into a paper. So I
wondered:
Is there actually a
difference between papers
that are accepted vs
rejected? Was there a way to
quantify that?
" Read more →
Featured
AI2er:
Chelsea
Haupt
We interview
Semantic Scholar
Product Manager
Chelsea Haupt
about what she's
been up to
lately, what
she's looking
forward to
building with
her team, and
more on the
AI2 Blog.
A guide to
language
model
sampling in
AllenNLP
Learn about how
Stochastic Beam
Search can add
~creativity~ to
your generated
text, in
this blog
post
from AI2 Intern
on the AllenNLP
team Jackson
Stokes.
Using AI to
Extract a
Knowledge
Base of
COVID-19
Mechanisms
Can we leverage
AI to help
researchers
navigate the
eclectic and
ever-growing
landscape of
scientific
literature
around COVID-19?
What if there
was a KB
containing
diverse and
structured
information on
causal
relations,
methods,
objectives, and
activities —
coming from any
scientific area?
Check out
this blog post
by AI2
researcher
Tom Hope
and try out the
COVID-19
Mechanism
Knowledge
Base
yourself.
Congratulations to AI2's top
interns of 2020!
We're thrilled to announce
the 2020 winners of the
AI2 Outstanding Intern
of the Year
award: Sarah Wiegreffe,
Sean MacAvaney,
and Unnat Jain. Thank
you for your excellent
contributions to AI2!
