论文标题
W-NUT 2020任务2：基于BERT的系统，用于识别信息丰富的COVID-19英语推文，
Not-NUTs at W-NUT 2020 Task 2: A BERT-based System in Identifying Informative COVID-19 English Tweets
论文作者
论文摘要
截至2020年，当COVID-19大流行在全球范围内成熟时，人们需要获得有关Covid-19的合法信息的需求，这比以往任何时候都更加紧迫，尤其是通过在线媒体中，大量无关的信息掩盖了更多信息信息。为此，我们提出了一个模型，鉴于英文推文，该模型会自动识别该推文是否具有有关COVID-19的信息内容。通过结合不同的Bertweet模型配置，我们取得了竞争成果，而竞争成果仅与最高表现的球队相比，在内容丰富的课程中，F1得分大约是1％。在竞争后期，我们还尝试了其他各种可能提高对新数据集的概括的方法。
As of 2020 when the COVID-19 pandemic is full-blown on a global scale, people's need to have access to legitimate information regarding COVID-19 is more urgent than ever, especially via online media where the abundance of irrelevant information overshadows the more informative ones. In response to such, we proposed a model that, given an English tweet, automatically identifies whether that tweet bears informative content regarding COVID-19 or not. By ensembling different BERTweet model configurations, we have achieved competitive results that are only shy of those by top performing teams by roughly 1% in terms of F1 score on the informative class. In the post-competition period, we have also experimented with various other approaches that potentially boost generalization to a new dataset.
