30 citations · 30 across the 1 of their papers we have counts for
6 papers
TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection
Ellen Voorhees, Tasmeer Alam, Steven Bedrick +6
TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pand…
Don't Stop Pretraining: Adapt Language Models to Domains and Tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta +4
Language models pretrained on text from a wide variety of sources form the foundation of today's NLP. In light of the success of these broad-coverage models, we investigate whether…
Fact or Fiction: Verifying Scientific Claims
David Wadden, Shanchuan Lin, Kyle Lo +4
We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that SUPPORTS or REFUTES a given scientific claim, and t…
CORD-19: The COVID-19 Open Research Dataset
Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar +25
The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research. CORD-19 is designed to facilitate t…
S2ORC: The Semantic Scholar Open Research Corpus
Kyle Lo, Lucy Lu Wang, Mark Neumann +2
We introduce S2ORC, a large corpus of 81.1M English-language academic papers spanning many academic disciplines. The corpus consists of rich metadata, paper abstracts, resolved bib…
SciBERT: A Pretrained Language Model for Scientific Text
Iz Beltagy, Kyle Lo, Arman Cohan
Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained language model based on BERT (Devlin et a…