Not-NUTs at W-NUT 2020 Task 2: A BERT-based System in Identifying Informative COVID-19 English Tweets
This addresses the need for filtering legitimate COVID-19 information in online media, but it is incremental as it builds on existing BERT-based methods for a specific task.
The paper tackled the problem of automatically identifying informative COVID-19 English tweets by proposing a model based on ensembling BERTweet configurations, achieving competitive results with F1 scores only about 1% lower than top performers.
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.