# Answering Questions on COVID-19 in Real-Time

arXiv: https://arxiv.org/abs/2006.15830 · HTML: https://mlsanity.com/paper/2006.15830/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

## Built on by (most cited first, 11 of 11)

- [RealTime QA: What's the Answer Right Now?](https://mlsanity.com/paper/2207.13332/) (2022)
- [Pre-trained Language Models in Biomedical Domain: A Systematic Survey](https://mlsanity.com/paper/2110.05006/) (2021)
- [COVID-19 Named Entity Recognition for Vietnamese](https://mlsanity.com/paper/2104.03879/) (2021)
- [BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks](https://mlsanity.com/paper/2109.02237/) (2021)
- [Synthetic Target Domain Supervision for Open Retrieval QA](https://mlsanity.com/paper/2204.09248/) (2022)
- [Review of Natural Language Processing in Pharmacology](https://mlsanity.com/paper/2208.10228/) (2022)
- [Augmenting Biomedical Named Entity Recognition with General-domain Resources](https://mlsanity.com/paper/2406.10671/) (2024)
- [COVIDRead: A Large-scale Question Answering Dataset on COVID-19](https://mlsanity.com/paper/2110.09321/) (2021)
- [Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains](https://mlsanity.com/paper/2201.11153/) (2022)
- [Enhancing Label Consistency on Document-level Named Entity Recognition](https://mlsanity.com/paper/2210.12949/) (2022)
- [Medical Literature Mining and Retrieval in a Conversational Setting](https://mlsanity.com/paper/2108.01436/) (2021)

## Abstract (arXiv)

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.