NutCracker at WNUT-2020 Task 2: Robustly Identifying Informative COVID-19 Tweets using Ensembling and Adversarial Training

TL;DR

This paper presents an ensemble approach using COVID-Twitter-BERT and RoBERTa, enhanced with adversarial training, to accurately identify informative COVID-19 tweets, achieving top performance in the WNUT-2020 Task 2 competition.

Contribution

The paper introduces a robust ensemble method combining COVID-Twitter-BERT and RoBERTa, incorporating adversarial training to improve COVID-19 tweet informativeness classification.

Findings

Achieved an F1-score of 0.9096 on the test set.

Ensemble models outperformed individual models.

Adversarial training maintained high performance.

Abstract

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.