@inproceedings{dhana-laxmi-etal-2020-dsc,
    title = "{DSC}-{IIT} {ISM} at {WNUT}-2020 Task 2: Detection of {COVID}-19 informative tweets using {R}o{BERT}a",
    author = "Dhana Laxmi, Sirigireddy  and
      Agarwal, Rohit  and
      Sinha, Aman",
    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/jlcl-multiple-ingestion/2020.wnut-1.58/",
    doi = "10.18653/v1/2020.wnut-1.58",
    pages = "409--413",
    abstract = "Social media such as Twitter is a hotspot of user-generated information. In this ongoing Covid-19 pandemic, there has been an abundance of data on social media which can be classified as informative and uninformative content. In this paper, we present our work to detect informative Covid-19 English tweets using RoBERTa model as a part of the W-NUT workshop 2020. We show the efficacy of our model on a public dataset with an F1-score of 0.89 on the validation dataset and 0.87 on the leaderboard."
}