Where Was COVID-19 First Discovered? Designing a Question-Answering System for Pandemic Situations
This addresses the challenge of finding credible information for COVID-19-related questions, though it is incremental as it applies existing NLP methods to a specific domain.
The paper tackled the problem of information overload and misinformation during the COVID-19 pandemic by designing a question-answering system using natural language processing, and demonstrated its usefulness by evaluating answer quality with biomedical experts on the CORD-19 dataset.
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.