*Date:* Thursday, 7 October 2021
*Location*: Virtual, co-located with AKBC 2021
*Contact Email: *cskb-akbc21(a)googlegroups.com
*Website:*
http://akbc-cskb.github.io/
Recent advances in large pre-trained language models have shown that
machines can directly learn large quantities of commonsense knowledge
through self-supervised learning on raw text. However, they still fall
short of human-like understanding capabilities: they make inconsistent
predictions, learn to exploit spurious patterns, and fail to robustly apply
learned knowledge to downstream applications. Consequently, the development
and integration of unpaired, outside knowledge representation sources
remains critically important to provide machine commonsense engines with
scaffolding to learn structured reasoning. We organize this workshop to
encourage discussion of current progress on building machines with
commonsense knowledge and reasoning abilities, with special focus on
commonsense knowledge bases (CSKBs).
*** Topics of Interest ***
Topics of interest include, but are not limited to:
* Resources: acquiring commonsense knowledge (from text corpora, images,
videos, pre-trained neural models, etc.); constructing and completing
(semi-)structured CSKBs; consolidating CSKBs under unified schemas.
* Benchmarks: designing challenging tasks and building datasets to evaluate
models' commonsense knowledge and reasoning abilities; designing new
evaluation schemas and metrics for commonsense reasoning tasks,
particularly for open-ended and generative tasks.
* Methods: methods for commonsense reasoning tasks; methods that integrate
CSKBs and neural models; methods that use CSKBs to improve the
interpretability and explainability of neural models for commonsense
reasoning and more.
* Analysis: methods to probe commonsense knowledge from NLP models; methods
to understand reasoning mechanisms of existing methods; methods that
identify limitations of existing methods for AI applications (including but
not limited to NLP, CV and robotics) due to lack of commonsense knowledge.
*** Submission Information ***
Papers should be submitted in OpenReview:
https://openreview.net/group?id=AKBC.ws/2021/Workshop/CSKB.
We solicit two categories of papers:
* Workshop papers: describing new, previously unpublished research in this
field. The submissions should follow the AKBC 2021 style guidelines:
https://github.com/akbc-conference/style-files/blob/master/akbc-latex.zi...
and contain up to 10 pages, excluding references and appendices (which
should be put after references). Submissions will be subject to a
single-blind review process (i.e. they need not be anonymized). Final
versions of accepted papers will be allowed 1 additional page of content so
that reviewer comments can be taken into account.
* Papers on topics relevant to the workshop theme, previously published at
NLP or ML conferences. These papers can be submitted in their original
format. Submissions will be reviewed for fit to the workshop topics.
In both categories, accepted papers will be published on the workshop
website (note that the workshop is non-archival), and will be presented at
the workshop either as a talk or a poster.
*** Important Dates ***
* All paper submissions due – August 5, 2021
* Notification of acceptance – Sep 10, 2021
* Camera-ready papers due – September 30, 2021
* Workshop – October 7, 2021
*** CSKB 2021 Keynote Speakers ***
We’re excited to have the following keynote speakers at CSKB 2021:
* Rachel Rudinger, University of Maryland
* Sara Hooker, Google Brain
* Maarten Sap, Allen Institute for AI (AI2) and CMU
* Xiang Ren, University of Southern California
We will also be holding a panel discussion with the invited speakers as
well as the following panelists:
* Mohit Bansal, University of North Carolina at Chapel Hill
* Greg Durrett, UT Austin
* Sameer Singh, UC Irvine
* Aida Nematzadeh, DeepMind
*** Organizing Committee ***
Vered Shwartz, Allen Institute for AI (AI2), University of Washington, and
University of British Columbia
Antoine Bosselut, Stanford University and EPFL
Xiang Lorraine Li, UMass Amherst
Bill Yuchen Lin, University of Southern California