Abstract
We describe our submissions to the 6th edition of the Social Media Mining for Health Applications (SMM4H) shared task. Our team (OGNLP) participated in the sub-task: Classification of tweets self-reporting potential cases of COVID-19 (Task 5). For our submissions, we employed systems based on auto-regressive transformer models (XLNet) and back-translation for balancing the dataset.- Anthology ID:
- 2021.smm4h-1.32
- 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:
- 146–148
- Language:
- URL:
- https://aclanthology.org/2021.smm4h-1.32
- DOI:
- 10.18653/v1/2021.smm4h-1.32
- Cite (ACL):
- Joseph Cornelius, Tilia Ellendorff, and Fabio Rinaldi. 2021. Approaching SMM4H with auto-regressive language models and back-translation. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 146–148, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Approaching SMM4H with auto-regressive language models and back-translation (Cornelius et al., SMM4H 2021)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2021.smm4h-1.32.pdf