NutCracker at WNUT-2020 Task 2: Robustly Identifying Informative COVID-19 Tweets using Ensembling and Adversarial Training
This work addresses the challenge of filtering relevant COVID-19 information on social media for public health monitoring, but it is incremental as it builds on existing models with ensembling and adversarial training.
The paper tackled the problem of identifying informative COVID-19 tweets by experimenting with COVID-Twitter-BERT and RoBERTa models, achieving a top-ranking F1-score of 0.9096 on the positive class in the WNUT-2020 Task 2 test data.
We experiment with COVID-Twitter-BERT and RoBERTa models to identify informative COVID-19 tweets. We further experiment with adversarial training to make our models robust. The ensemble of COVID-Twitter-BERT and RoBERTa obtains a F1-score of 0.9096 (on the positive class) on the test data of WNUT-2020 Task 2 and ranks 1st on the leaderboard. The ensemble of the models trained using adversarial training also produces similar result.