Not-NUTs at WNUT-2020 Task 2: A BERT-based System in Identifying Informative COVID-19 English Tweets
Abstract
As of 2020 when the COVID-19 pandemic is full-blown on a global scale, people’s need to have access to legitimate information regarding COVID-19 is more urgent than ever, especially via online media where the abundance of irrelevant information overshadows the more informative ones. In response to such, we proposed a model that, given an English tweet, automatically identifies whether that tweet bears informative content regarding COVID-19 or not. By ensembling different BERTweet model configurations, we have achieved competitive results that are only shy of those by top performing teams by roughly 1% in terms of F1 score on the informative class. In the post-competition period, we have also experimented with various other approaches that potentially boost generalization to a new dataset.- Anthology ID:
- 2020.wnut-1.69
- 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:
- 466–470
- Language:
- URL:
- https://aclanthology.org/2020.wnut-1.69
- DOI:
- 10.18653/v1/2020.wnut-1.69
- Cite (ACL):
- Thai Hoang and Phuong Vu. 2020. Not-NUTs at WNUT-2020 Task 2: A BERT-based System in Identifying Informative COVID-19 English Tweets. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 466–470, Online. Association for Computational Linguistics.
- Cite (Informal):
- Not-NUTs at WNUT-2020 Task 2: A BERT-based System in Identifying Informative COVID-19 English Tweets (Hoang & Vu, WNUT 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.wnut-1.69.pdf
- Data
- WNUT-2020 Task 2