The COVID-19 pandemic is accompanied by a massive "infodemic" that makes it
hard to identify concise and credible information for COVID-19-related
questions, like incubation time, infection rates, or the effectiveness of
vaccines. As a novel solution, our paper is concerned with designing a
question-answering system based on modern technologies from natural language
processing to overcome information overload and misinformation in pandemic
situations. To carry out our research, we followed a design science research
approach and applied Ingwersen's cognitive model of information retrieval
interaction to inform our design process from a socio-technical lens. On this
basis, we derived prescriptive design knowledge in terms of design requirements
and design principles, which we translated into the construction of a
prototypical instantiation. Our implementation is based on the comprehensive
CORD-19 dataset, and we demonstrate our artifact's usefulness by evaluating its
answer quality based on a sample of COVID-19 questions labeled by biomedical
experts.

本研究采用现代自然语言处理技术设计了一个基于问题回答系统，以应对 COVID-19 大规模信息泛滥和不实传言。我们依据 Ingwersen 的信息检索认知模型，从社会技术角度推导并转化成具体的设计要求和设计原则，并以 CORD-19 数据集为基础构建了一个样机系统。我们根据生物医学专家标记的 COVID-19 问题样本，评估了系统的答案质量，证明了其有用性。