论文标题
COV19IR：COVID-19域文献信息检索
COV19IR : COVID-19 Domain Literature Information Retrieval
论文作者
论文摘要
越来越多的Covid-19研究文献在有效的文献筛选和COVID-19域知识知识信息检索中引起了新的挑战。为了应对挑战，我们将展示两项任务以及溶解，即19.19文献检索和问题回答。 COVID-19文献检索任务屏幕匹配Covid-19的文本用户查询文献文档，而COVID-19的问题回答任务可以预测文本语料库中的适当文本片段，作为特定COVID-19相关问题的答案。基于变压器神经网络，我们提供了解决CORD-19数据集上的任务的解决方案，我们显示了一些示例，以显示我们建议的解决方案的有效性。
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.
