论文标题
W-NUT 2020任务2：基于变压器的基线系统，用于识别信息丰富的COVID-19英语推文
TATL at W-NUT 2020 Task 2: A Transformer-based Baseline System for Identification of Informative COVID-19 English Tweets
论文作者
论文摘要
随着Covid-19-19爆发继续在全球范围内传播，有关大流行的越来越多的信息已在社交媒体上公开分享。例如，每天在Twitter上有大量Covid-19英语推文。但是，这些推文中的大多数都是无信息的，因此，能够自动为下游应用程序自动选择内容丰富的信息很重要。在这篇简短的论文中，我们介绍了参与W-NUT 2020共享任务2：识别COVID-19英语推文的识别。受到验证的变压器语言模型的最新进展的启发，我们为任务提出了一个简单而有效的基准。尽管它很简单，但我们提出的方法在排行榜中表现出非常具竞争力的结果，因为我们在56支球队中排名8的总数参加了总数。
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.
