LT4SG@SMM4H24: Tweets Classification for Digital Epidemiology of Childhood Health Outcomes Using Pre-Trained Language Models
<|reference_start|>LT4SG@SMM4H24: Tweets Classification for Digital Epidemiology of Childhood Health Outcomes Using Pre-Trained Language Models: This paper presents our approaches for the SMM4H24 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%.<|reference_end|>
@article{athukoralage2024lt4sg@smm4h24:,
    title={LT4SG@SMM4H24: Tweets Classification for Digital Epidemiology of
  Childhood Health Outcomes Using Pre-Trained Language Models},
    author={Dasun Athukoralage, Thushari Atapattu, Menasha Thilakaratne and
  Katrina Falkner},
    journal={arXiv preprint arXiv:2406.07759},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07759},
    primaryClass={cs.CL}
}