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The competition of extracting COVID-19 events from Twitter is to developsystems that can automatically extract related events from tweets. The builtsystem should identify different pre-defined slots for each event, in order toanswer important questions (e.g., Who is tested positive? What is the age ofthe person? Where is he/she?). To tackle these challenges, we propose the JointEvent Multi-task Learning (JOELIN) model. Through a unified global learningframework, we make use of all the training data across different events tolearn and fine-tune the language model. Moreover, we implement a type-awarepost-processing procedure using named entity recognition (NER) to furtherfilter the predictions. JOELIN outperforms the BERT baseline by 17.2% in microF1.

TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text