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The novel coronavirus disease 2019 (COVID-19) began in Wuhan, China in late2019 and to date has infected over 14M people worldwide, resulting in over750,000 deaths. On March 10, 2020 the World Health Organization (WHO) declaredthe outbreak a global pandemic. Many academics and researchers, not restrictedto the medical domain, began publishing papers describing new discoveries.However, with the large influx of publications, it was hard for theseindividuals to sift through the large amount of data and make sense of thefindings. The White House and a group of industry research labs, lead by theAllen Institute for AI, aggregated over 200,000 journal articles related to avariety of coronaviruses and tasked the community with answering key questionsrelated to the corpus, releasing the dataset as CORD-19. The informationretrieval (IR) community repurposed the journal articles within CORD-19 to moreclosely resemble a classic TREC-style competition, dubbed TREC-COVID, withhuman annotators providing relevancy judgements at the end of each round ofcompetition. Seeing the related endeavors, we set out to repurpose therelevancy annotations for TREC-COVID tasks to identify journal articles inCORD-19 which are relevant to the key questions posed by CORD-19. A BioBERTmodel trained on this repurposed dataset prescribes relevancy annotations forCORD-19 tasks that have an overall agreement of 0.4430 with majority humanannotations in terms of Cohen's kappa. We present the methodology used toconstruct the new dataset and describe the decision process used throughout.

Repurposing TREC-COVID Annotations to Answer the Key Questions of CORD-19