COV19IR : COVID-19 Domain Literature Information Retrieval
This addresses the problem of managing the growing volume of COVID-19 literature for researchers and practitioners, but it is incremental as it applies existing transformer methods to a specific domain.
The authors tackled the challenge of screening and retrieving COVID-19 research literature by developing tasks for literature retrieval and question answering, demonstrating their effectiveness on the CORD-19 dataset with examples.
Increasing number of COVID-19 research literatures cause new challenges in effective literature screening and COVID-19 domain knowledge aware Information Retrieval. To tackle the challenges, we demonstrate two tasks along withsolutions, COVID-19 literature retrieval, and question answering. COVID-19 literature retrieval task screens matching COVID-19 literature documents for textual user query, and COVID-19 question answering task predicts proper text fragments from text corpus as the answer of specific COVID-19 related questions. Based on transformer neural network, we provided solutions to implement the tasks on CORD-19 dataset, we display some examples to show the effectiveness of our proposed solutions.