UIT-HSE at WNUT-2020 Task 2: Exploiting CT-BERT for Identifying COVID-19 Information on the Twitter Social Network

TL;DR

This paper describes a transformer-based approach using CT-BERT to identify informative COVID-19 tweets, achieving high accuracy and ranking third in a shared task competition.

Contribution

The paper introduces a fine-tuning method of CT-BERT for COVID-19 tweet classification, demonstrating its effectiveness in a competitive setting.

Findings

Achieved an F1-score of 90.94% on the task

Secured third place among 56 teams

Showed the effectiveness of transformer fine-tuning for COVID-19 tweet classification

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.