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As the COVID-19 outbreak continues to spread throughout the world, more andmore information about the pandemic has been shared publicly on social media.For example, there are a huge number of COVID-19 English Tweets daily onTwitter. However, the majority of those Tweets are uninformative, and hence itis important to be able to automatically select only the informative ones fordownstream applications. In this short paper, we present our participation inthe W-NUT 2020 Shared Task 2: Identification of Informative COVID-19 EnglishTweets. Inspired by the recent advances in pretrained Transformer languagemodels, we propose a simple yet effective baseline for the task. Despite itssimplicity, our proposed approach shows very competitive results in theleaderboard as we ranked 8 over 56 teams participated in total.

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