Fine-tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets

Max Fleming, Priyanka Dondeti, Caitlin Dreisbach, Adam Poliak


Abstract
We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social Media Min- ing for Health Applications (SMM4H) shared tasks. Our system is based on fine-tuning Dis- tillBERT on each task, as well as first fine- tuning the model on the other task. In this paper, we additionally 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).
Anthology ID:
2021.smm4h-1.28
Volume:
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
Month:
June
Year:
2021
Address:
Mexico City, Mexico
Venue:
SMM4H
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
131–134
Language:
URL:
https://aclanthology.org/2021.smm4h-1.28
DOI:
10.18653/v1/2021.smm4h-1.28
Bibkey:
Cite (ACL):
Max Fleming, Priyanka Dondeti, Caitlin Dreisbach, and Adam Poliak. 2021. Fine-tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 131–134, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Fine-tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets (Fleming et al., SMM4H 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.smm4h-1.28.pdf
Code
 mfleming99/SMM4H_2021