A newly developed search system exhibits an 80% boost in accuracy and reliability for searches related to COVID-19 and other health topics, according to computer scientists at Canada's University of Waterloo.
"Most of the [search] systems are trained on well-curated data, so they don't always know how to differentiate between an article promoting drinking bleach to prevent COVID-19 as opposed to real health information," said Waterloo's Ronak Pradeep. "Our goal is to help people see the right articles and get the right information so they can make better decisions in general with things like COVID."
Researchers augmented their two-stage neural reranking architecture, known as mono-duo-T5, with the label prediction system Vera, which is trained to distinguish between correct and incorrect information. A search protocol relying on data from the World Health Organization and verified information is linked to the system to help rank, promote, and exclude certain online articles.
From University of Waterloo News
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