@inproceedings{guo-etal-2020-emory,
    title = "Emory at {WNUT}-2020 Task 2: Combining Pretrained Deep Learning Models and Feature Enrichment for Informative Tweet Identification",
    author = "Guo, Yuting  and
      Ali Al-Garadi, Mohammed  and
      Sarker, Abeed",
    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-1.54/",
    doi = "10.18653/v1/2020.wnut-1.54",
    pages = "388--393",
    abstract = "This paper describes the system developed by the Emory team for the WNUT-2020 Task 2: {\textquotedblleft}Identifi- cation of Informative COVID-19 English Tweet{\textquotedblright}. Our system explores three recent Transformer- based deep learning models pretrained on large- scale data to encode documents. Moreover, we developed two feature enrichment methods to en- hance document embeddings by integrating emoji embeddings and syntactic features into deep learn- ing models. Our system achieved F1-score of 0.897 and accuracy of 90.1{\%} on the test set, and ranked in the top-third of all 55 teams."
}