Answering Questions on COVID-19 in Real-Time

Jinhyuk Lee, Sean S. Yi, Minbyul Jeong, Mujeen Sung, WonJin Yoon, Yonghwa Choi, Miyoung Ko, Jaewoo Kang


Abstract
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.
Anthology ID:
2020.nlpcovid19-2.1
Volume:
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
Month:
December
Year:
2020
Address:
Online
Venues:
EMNLP | NLP-COVID19
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
Language:
URL:
https://aclanthology.org/2020.nlpcovid19-2.1
DOI:
10.18653/v1/2020.nlpcovid19-2.1
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.nlpcovid19-2.1.pdf
Video:
 https://slideslive.com/38939843
Code
 dmis-lab/covidAsk
Data
CORD-19Natural QuestionsSQuAD