@inproceedings{chanda-etal-2020-irlab,
    title = "{IRL}ab@{IITBHU} at {WNUT}-2020 Task 2: Identification of informative {COVID}-19 {E}nglish Tweets using {BERT}",
    author = "Chanda, Supriya  and
      Nandy, Eshita  and
      Pal, Sukomal",
    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_wac_2008/2020.wnut-1.56/",
    doi = "10.18653/v1/2020.wnut-1.56",
    pages = "399--403",
    abstract = "This paper reports our submission to the shared Task 2: Identification of informative COVID-19 English tweets at W-NUT 2020. We attempted a few techniques, and we briefly explain here two models that showed promising results in tweet classification tasks: DistilBERT and FastText. DistilBERT achieves a F1 score of 0.7508 on the test set, which is the best of our submissions."
}