论文标题
WNUT-2020任务2：利用CT-Bert来识别Twitter社交网络上的COVID-19信息
UIT-HSE at WNUT-2020 Task 2: Exploiting CT-BERT for Identifying COVID-19 Information on the Twitter Social Network
论文作者
论文摘要
最近，Covid-19影响了世界各种现实生活，并带来了可怕的后果。关于Covid-19的推文越来越多，已在Twitter上公开分享。但是，这些推文的多种多数是无信息的，这是构建自动系统以检测有用AI应用程序的信息系统的挑战。在本文中，我们在W-NUT 2020共享任务2上介绍了结果2：信息丰富的Covid-19英语推文的标识。特别是，我们使用基于COVID-TWITTER-BERT（CT-BERT）的基于变压器的模型（使用不同的微调技术）提出了简单但有效的方法。结果，我们达到了90.94 \％的F1得分，在该任务的排行榜上排名第三，这吸引了56个提交的团队。
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.
