@inproceedings{huynh-etal-2020-banana,
    title = "{BANANA} at {WNUT}-2020 Task 2: Identifying {COVID}-19 Information on {T}witter by Combining Deep Learning and Transfer Learning Models",
    author = "Huynh, Tin  and
      Thanh Luan, Luan  and
      Luu, Son T.",
    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-1.50/",
    doi = "10.18653/v1/2020.wnut-1.50",
    pages = "366--370",
    abstract = "The outbreak COVID-19 virus caused a significant impact on the health of people all over the world. Therefore, it is essential to have a piece of constant and accurate information about the disease with everyone. This paper describes our prediction system for WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets. The dataset for this task contains size 10,000 tweets in English labeled by humans. The ensemble model from our three transformer and deep learning models is used for the final prediction. The experimental result indicates that we have achieved F1 for the INFORMATIVE label on our systems at 88.81{\%} on the test set."
}