The recent outbreak of the novel coronavirus is wreaking havoc on the world and researchers are struggling to effectively combat it. One reason why the fight is difficult is due to the lack of information and knowledge. In this work, we outline our effort to contribute to shrinking this knowledge vacuum by creating covidAsk, a question answering (QA) system that combines biomedical text mining and QA techniques to provide answers to questions in real-time. Our system also leverages information retrieval (IR) approaches to provide entity-level answers that are complementary to QA models. Evaluation of covidAsk is carried out by using a manually created dataset called COVID-19 Questions which is based on information from various sources, including the CDC and the WHO. We hope our system will be able to aid researchers in their search for knowledge and information not only for COVID-19, but for future pandemics as well.

中文翻译：

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实时回答有关 COVID-19 的问题

最近爆​​发的新型冠状病毒正在对世界造成严重破坏，研究人员正在努力有效地对抗它。战斗困难的原因之一是缺乏信息和知识。在这项工作中，我们概述了我们通过创建 covidAsk 来为缩小这种知识真空做出贡献的努力，covidAsk 是一种问答 (QA) 系统，该系统结合了生物医学文本挖掘和 QA 技术，可以实时提供问题的答案。我们的系统还利用信息检索 (IR) 方法来提供与 QA 模型互补的实体级答案。covidAsk 的评估是通过使用手动创建的名为 COVID-19 Questions 的数据集进行的，该数据集基于来自包括 CDC 和 WHO 在内的各种来源的信息。