Hackathon: AI for Knowledge Discovery (NLP)

Challenge Description:Tens of thousands of research papers on the SARS-CoV-2 virus and the COVID-19 illness haveflooded journals and preprint servers, with many more added every day. This publication rate isleaving researchers unable to keep up with new findings and insights, even as they rely on theseto inform their own work in understanding and combating the disease. In particular, researchgroups investigating novel drug candidates with in-silico drug discovery efforts are in essencesearching for a proverbial needle in a haystack, and so rely on scientific insights from theliterature to guide their efforts toward the most fruitful directions. Thus, they are left to navigatethe current publication deluge for their primary guidance. In order to keep pace with the vastamount of relevant literature that continues to grow, researchers need more capable tools tolocate and filter the information they seek in these publications. To meet this pressing need, it isdesirable to develop and apply the latest Natural Language Processing (NLP) techniques forefficiently locating and filtering relevant information from a growing dataset of COVID-relatedliterature, to help accelerate drug discovery research. The proposed NLP challenge task is framedtowards designing and building a Question-Answering (QA) system to find answers to COVID-related questions in the scientific literature.Goal:A practical usage of NLP models with COVID relevant papers might be automated informationextraction from literature to facilitate drug discovery efforts. One of the crucial elements thatcan inform these efforts is the knowledge about viral proteins. The goal of this data challenge tobuild an NLP model to identify answers to protein-related questions from scientific papers.