Abstract
As the COVID-19 outbreak continues to spread throughout the world, more and more information about the pandemic has been shared publicly on social media. For example, there are a huge number of COVID-19 English Tweets daily on Twitter. However, the majority of those Tweets are uninformative, and hence it is important to be able to automatically select only the informative ones for downstream applications. In this short paper, we present our participation in the W-NUT 2020 Shared Task 2: Identification of Informative COVID-19 English Tweets. Inspired by the recent advances in pretrained Transformer language models, we propose a simple yet effective baseline for the task. Despite its simplicity, our proposed approach shows very competitive results in the leaderboard as we ranked 8 over 56 teams participated in total.- Anthology ID:
- 2020.wnut-1.42
- 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:
- 319–323
- Language:
- URL:
- https://aclanthology.org/2020.wnut-1.42
- DOI:
- 10.18653/v1/2020.wnut-1.42
- Cite (ACL):
- Anh Tuan Nguyen. 2020. TATL at WNUT-2020 Task 2: A Transformer-based Baseline System for Identification of Informative COVID-19 English Tweets. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 319–323, Online. Association for Computational Linguistics.
- Cite (Informal):
- TATL at WNUT-2020 Task 2: A Transformer-based Baseline System for Identification of Informative COVID-19 English Tweets (Tuan Nguyen, WNUT 2020)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2020.wnut-1.42.pdf