  The competition of extracting COVID-19 events from Twitter is to develop
systems that can automatically extract related events from tweets. The built
system should identify different pre-defined slots for each event, in order to
answer important questions (e.g., Who is tested positive? What is the age of
the person? Where is he/she?). To tackle these challenges, we propose the Joint
Event Multi-task Learning (JOELIN) model. Through a unified global learning
framework, we make use of all the training data across different events to
learn and fine-tune the language model. Moreover, we implement a type-aware
post-processing procedure using named entity recognition (NER) to further
filter the predictions. JOELIN outperforms the BERT baseline by 17.2% in micro
F1.
