Answering Questions on COVID-19 in Real-Time
arXiv:2006.15830
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.
arXiv comment10 pages, EMNLP NLP-COVID Workshop 2020
References in corpus (12)
- BioBERT: a pre-trained biomedical language representation model for biomedical text mining
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- Language Models are Few-Shot Learners
- Teaching Machines to Read and Comprehend
- Bidirectional Attention Flow for Machine Comprehension
- CORD-19: The COVID-19 Open Research Dataset
- Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering
- Unsupervised Question Answering by Cloze Translation
- Differentiable Reasoning over a Virtual Knowledge Base
- Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset: Preliminary Thoughts and Lessons Learned
- TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection
- What Are People Asking About COVID-19? A Question Classification Dataset