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In this edition: Updates on the CORD-19 effort and the new tools being built to explore this important resource, a demo of the reasoning capabilities of language transformers, exciting new state-of-the-art QA work from the Aristo team, and more.

New Developments in Semantic Scholar's CORD-19 resource

On March 16, our Semantic Scholar team and collaborators released the COVID-19 Open Research Dataset (CORD-19), along with a new challenge for the research community in partnership with Kaggle (learn more in this special edition of the AI2 Newsletter). In the weeks since, hundreds of organizations and thousands of people have contributed to the project, and the associated Kaggle competition has become the most popular ever on the platform.

CORD-19 is a model for the power of open science combined with cutting-edge AI in the face of a global crisis. Learn more in this article from AI2 CEO Oren Etzioni: 


An NLP power tool for extractive search over CORD-19 using a variety of query modes. It allows a level of query expressivity and control that is substantially more powerful than existing search solutions. Read the blog post from Yoav Goldberg, Research Director at AI2 Israel, to learn more.


SciSight is a new framework of tools for visualizing the emerging literature network around COVID-19. Find out what groups are working on what directions, see how biomedical concepts interact and evolve over time, and discover new connections. Learn more in a new blog post from the AI2 researchers behind SciSight.
Try SciSight
New in NLP

Can Language Transformers Do Reasoning?

The Aristo team at AI2 released a demo of their new interactive tool RuleTaker that determines whether statements are True or False based on rules provided in natural language. Try the demo, and learn more about the research behind RuleTaker: 

UnifiedQA: state-of-the-art results on six QA datasets

Researchers on the Aristo team used the latest advances in language modeling to build a single pre-trained question answering model, UnifiedQA, that performs surprisingly well across 17 QA datasets spanning 4 diverse formats (and achieves state-of-the-art results on 6 of those datasets!) – try the demo, and learn more:
In the News
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