@inproceedings{kumar-singh-2020-nutcracker,
    title = "{N}ut{C}racker at {WNUT}-2020 Task 2: Robustly Identifying Informative {COVID}-19 Tweets using Ensembling and Adversarial Training",
    author = "Kumar, Priyanshu  and
      Singh, Aadarsh",
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.wnut-1.57/",
    doi = "10.18653/v1/2020.wnut-1.57",
    pages = "404--408",
    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."
}