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Twitter has acted as an important source of information during disasters andpandemic, especially during the times of COVID-19. In this paper, we describeour system entry for WNUT 2020 Shared Task-3. The task was aimed at automatingthe extraction of a variety of COVID-19 related events from Twitter, such asindividuals who recently contracted the virus, someone with symptoms who weredenied testing and believed remedies against the infection. The system consistsof separate multi-task models for slot-filling subtasks andsentence-classification subtasks while leveraging the useful sentence-levelinformation for the corresponding event. The system uses COVID-Twitter-Bertwith attention-weighted pooling of candidate slot-chunk features to capture theuseful information chunks. The system ranks 1st at the leader-board with F1 of0.6598, without using any ensembles or additional datasets. The code andtrained models are available at this https URL.

Leveraging Event Specific and Chunk Span features to Extract COVID Events from tweets