Abstract
In this paper, we describe our system entry for Shared Task 8 at SMM4H-2021, which is on automatic classification of self-reported breast cancer posts on Twitter. In our system, we use a transformer-based language model fine-tuning approach to automatically identify tweets in the self-reports category. Furthermore, we involve a Gradient-based Adversarial fine-tuning to improve the overall model’s robustness. Our system achieved an F1-score of 0.8625 on the Development set and 0.8501 on the Test set in Shared Task-8 of SMM4H-2021.- Anthology ID:
- 2021.smm4h-1.22
- 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:
- 112–114
- Language:
- URL:
- https://aclanthology.org/2021.smm4h-1.22
- DOI:
- 10.18653/v1/2021.smm4h-1.22
- Cite (ACL):
- Adarsh Kumar, Ojasv Kamal, and Susmita Mazumdar. 2021. Adversities are all you need: Classification of self-reported breast cancer posts on Twitter using Adversarial Fine-tuning. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 112–114, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Adversities are all you need: Classification of self-reported breast cancer posts on Twitter using Adversarial Fine-tuning (Kumar et al., SMM4H 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2021.smm4h-1.22.pdf
- Data
- BLUE