Approaching SMM4H with auto-regressive language models and back-translation

Joseph Cornelius, Tilia Ellendorff, Fabio Rinaldi


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
Editors:
Arjun Magge, Ari Klein, Antonio Miranda-Escalada, Mohammed Ali Al-garadi, Ilseyar Alimova, Zulfat Miftahutdinov, Eulalia Farre-Maduell, Salvador Lima Lopez, Ivan Flores, Karen O'Connor, Davy Weissenbacher, Elena Tutubalina, Abeed Sarker, Juan M Banda, Martin Krallinger, Graciela Gonzalez-Hernandez
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2021.smm4h-1.32.pdf