@inproceedings{athukoralage-etal-2024-lt4sg,
    title = "{LT}4{SG}@{SMM}4{H}`24: Tweets Classification for Digital Epidemiology of Childhood Health Outcomes Using Pre-Trained Language Models",
    author = "Athukoralage, Dasun  and
      Atapattu, Thushari  and
      Thilakaratne, Menasha  and
      Falkner, Katrina",
    editor = "Xu, Dongfang  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.smm4h-1.9/",
    pages = "38--41",
    abstract = "This paper presents our approaches for the SMM4H`24 Shared Task 5 on the binary classification of English tweets reporting children`s medical disorders. Our first approach involves fine-tuning a single RoBERTa-large model, while the second approach entails ensembling the results of three fine-tuned BERTweet-large models. We demonstrate that although both approaches exhibit identical performance on validation data, the BERTweet-large ensemble excels on test data. Our best-performing system achieves an F1-score of 0.938 on test data, outperforming the benchmark classifier by 1.18{\%}."
}