@inproceedings{hoang-vu-2020-nuts,
    title = "Not-{NUT}s at {WNUT}-2020 Task 2: A {BERT}-based System in Identifying Informative {COVID}-19 {E}nglish Tweets",
    author = "Hoang, Thai  and
      Vu, Phuong",
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.wnut-1.69/",
    doi = "10.18653/v1/2020.wnut-1.69",
    pages = "466--470",
    abstract = "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."
}