  Twitter has acted as an important source of information during disasters and
pandemic, especially during the times of COVID-19. In this paper, we describe
our system entry for WNUT 2020 Shared Task-3. The task was aimed at automating
the extraction of a variety of COVID-19 related events from Twitter, such as
individuals who recently contracted the virus, someone with symptoms who were
denied testing and believed remedies against the infection. The system consists
of separate multi-task models for slot-filling subtasks and
sentence-classification subtasks while leveraging the useful sentence-level
information for the corresponding event. The system uses COVID-Twitter-Bert
with attention-weighted pooling of candidate slot-chunk features to capture the
useful information chunks. The system ranks 1st at the leader-board with F1 of
0.6598, without using any ensembles or additional datasets. The code and
trained models are available at this https URL.
