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.
翻译:越来越多的COVID-19研究文献在有效文献筛选和COVID-19领域知识知情信息检索方面带来了新的挑战。为了应对挑战,我们展示了两项任务以及解决方案,即COVID-19文献检索和回答问题。COVID-19文献检索任务屏幕与用于文字用户查询的COVID-19文献文件相匹配,而COVID-19回答问题则预测文本中适当的文本碎片是特定COVID-19相关问题的答案。根据变压器神经网络,我们提供了执行CORD-19数据集任务的解决办法,我们展示了一些例子,以表明我们拟议解决方案的有效性。