Abstract
We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social Media Mining for Health Applications (SMM4H) shared tasks. Our system is based on fine-tuning DistillBERT on each task, as well as first fine-tuning the model on the other task. We explore how much fine-tuning is necessary for accurately classifying tweets as containing self-reported COVID-19 symptoms (Task 5) or whether a tweet related to COVID-19 is self-reporting, non-personal reporting, or a literature/news mention of the virus (Task 6).
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CITATION STYLE
Fleming, M., Dondeti, P., Dreisbach, C. N., & Poliak, A. (2021). Fine-Tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets. In Social Media Mining for Health, SMM4H 2021 - Proceedings of the 6th Workshop and Shared Tasks (pp. 131–134). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.smm4h-1.28
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