@inproceedings{sancheti-etal-2020-lynyrdskynyrd,
    title = "{L}ynyrd{S}kynyrd at {WNUT}-2020 Task 2: Semi-Supervised Learning for Identification of Informative {COVID}-19 {E}nglish Tweets",
    author = "Sancheti, Abhilasha  and
      Chawla, Kushal  and
      Verma, Gaurav",
    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-1.65/",
    doi = "10.18653/v1/2020.wnut-1.65",
    pages = "444--449",
    abstract = "In this work, we describe our system for WNUT-2020 shared task on the identification of informative COVID-19 English tweets. Our system is an ensemble of various machine learning methods, leveraging both traditional feature-based classifiers as well as recent advances in pre-trained language models that help in capturing the syntactic, semantic, and contextual features from the tweets. We further employ pseudo-labelling to incorporate the unlabelled Twitter data released on the pandemic. Our best performing model achieves an F1-score of 0.9179 on the provided validation set and 0.8805 on the blind test-set."
}