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

Priyanshu Kumar, Aadarsh Singh


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.
Anthology ID:
2020.wnut-1.57
Volume:
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
Month:
November
Year:
2020
Address:
Online
Editors:
Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
404–408
Language:
URL:
https://aclanthology.org/2020.wnut-1.57
DOI:
10.18653/v1/2020.wnut-1.57
Bibkey:
Cite (ACL):
Priyanshu Kumar and Aadarsh Singh. 2020. NutCracker at WNUT-2020 Task 2: Robustly Identifying Informative COVID-19 Tweets using Ensembling and Adversarial Training. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 404–408, Online. Association for Computational Linguistics.
Cite (Informal):
NutCracker at WNUT-2020 Task 2: Robustly Identifying Informative COVID-19 Tweets using Ensembling and Adversarial Training (Kumar & Singh, WNUT 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.wnut-1.57.pdf
Code
 kpriyanshu256/WNUT-2020-Task-2
Data
WNUT-2020 Task 2