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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.wnut-1.57.pdf
- Code
- kpriyanshu256/WNUT-2020-Task-2
- Data
- WNUT-2020 Task 2