TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text
This work addresses the challenge of slot filling for event extraction in noisy social media data, which is incremental as it builds on existing language models with multi-task learning and post-processing.
The paper tackled the problem of extracting COVID-19 events from noisy Twitter text by developing a joint event multi-task learning model, which outperformed a BERT baseline by 17.2% in micro F1 score.
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.