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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.smm4h-1.28.pdf
- Code
- mfleming99/SMM4H_2021