UIT-HSE at WNUT-2020 Task 2: Exploiting CT-BERT for Identifying COVID-19 Information on the Twitter Social Network
Abstract
Recently, COVID-19 has affected a variety of real-life aspects of the world and led to dreadful consequences. More and more tweets about COVID-19 has been shared publicly on Twitter. However, the plurality of those Tweets are uninformative, which is challenging to build automatic systems to detect the informative ones for useful AI applications. In this paper, we present our results at the W-NUT 2020 Shared Task 2: Identification of Informative COVID-19 English Tweets. In particular, we propose our simple but effective approach using the transformer-based models based on COVID-Twitter-BERT (CT-BERT) with different fine-tuning techniques. As a result, we achieve the F1-Score of 90.94\% with the third place on the leaderboard of this task which attracted 56 submitted teams in total.
Keywords
Cite
@article{arxiv.2009.02935,
title = {UIT-HSE at WNUT-2020 Task 2: Exploiting CT-BERT for Identifying COVID-19 Information on the Twitter Social Network},
author = {Khiem Vinh Tran and Hao Phu Phan and Kiet Van Nguyen and Ngan Luu-Thuy Nguyen},
journal= {arXiv preprint arXiv:2009.02935},
year = {2020}
}
Comments
Accepted by 2020 The 6th Workshop on Noisy User-generated Text (W-NUT) - EMNLP 2020