NutCracker at WNUT-2020 Task 2: Robustly Identifying Informative COVID-19 Tweets using Ensembling and Adversarial Training
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Oct 2020.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1.
5 papers.
- 2021The Surprising Performance of Simple Baselines for Misinformation Detectioncited by 27
- 2021Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Taskcited by 6
- 2021UPB at SemEval-2021 Task 5: Virtual Adversarial Training for Toxic Spans Detectioncited by 1
- 2023Learning Mutually Informed Representations for Characters and Subwordscited by 1
- 2022Multilingual Abusiveness Identification on Code-Mixed Social Media Textcited by 0
- 2023Learning Mutually Informed Representations for Characters and Subwords14 Nov 2023
- 2022Multilingual Abusiveness Identification on Code-Mixed Social Media Text1 Mar 2022
- 2021UPB at SemEval-2021 Task 5: Virtual Adversarial Training for Toxic Spans Detection17 Apr 2021
- 2021The Surprising Performance of Simple Baselines for Misinformation Detection14 Apr 2021
- 2021Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task12 Jan 2021