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.
@article{arxiv.2211.04013,
title = {COV19IR : COVID-19 Domain Literature Information Retrieval},
author = {Arusarka Bose and Zili Zhou and Guandong Xu},
journal= {arXiv preprint arXiv:2211.04013},
year = {2022}
}