TATL at W-NUT 2020 Task 2: A Transformer-based Baseline System for Identification of Informative COVID-19 English Tweets

TL;DR

This paper introduces a Transformer-based baseline system for identifying informative COVID-19 English Tweets, achieving competitive results in the W-NUT 2020 shared task.

Contribution

It presents a simple, effective Transformer-based approach for classifying informative COVID-19 tweets, demonstrating strong performance in a shared task setting.

Findings

Ranked 8th out of 56 teams in the leaderboard

Achieved competitive results with a simple Transformer model

Proposed approach is effective for COVID-19 tweet classification

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.