@inproceedings{kumar-etal-2021-adversities,
    title = "Adversities are all you need: Classification of self-reported breast cancer posts on {T}witter using Adversarial Fine-tuning",
    author = "Kumar, Adarsh  and
      Kamal, Ojasv  and
      Mazumdar, Susmita",
    editor = "Magge, Arjun  and
      Klein, Ari  and
      Miranda-Escalada, Antonio  and
      Al-garadi, Mohammed Ali  and
      Alimova, Ilseyar  and
      Miftahutdinov, Zulfat  and
      Farre-Maduell, Eulalia  and
      Lopez, Salvador Lima  and
      Flores, Ivan  and
      O'Connor, Karen  and
      Weissenbacher, Davy  and
      Tutubalina, Elena  and
      Sarker, Abeed  and
      Banda, Juan M  and
      Krallinger, Martin  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the Sixth Social Media Mining for Health ({\#}SMM4H) Workshop and Shared Task",
    month = jun,
    year = "2021",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.smm4h-1.22/",
    doi = "10.18653/v1/2021.smm4h-1.22",
    pages = "112--114",
    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."
}