@inproceedings{saha-etal-2020-autobots,
    title = "Autobots Ensemble: Identifying and Extracting Adverse Drug Reaction from Tweets Using Transformer Based Pipelines",
    author = "Saha, Sougata  and
      Das, Souvik  and
      Khurana, Prashi  and
      Srihari, Rohini",
    editor = "Gonzalez-Hernandez, Graciela  and
      Klein, Ari Z.  and
      Flores, Ivan  and
      Weissenbacher, Davy  and
      Magge, Arjun  and
      O'Connor, Karen  and
      Sarker, Abeed  and
      Minard, Anne-Lyse  and
      Tutubalina, Elena  and
      Miftahutdinov, Zulfat  and
      Alimova, Ilseyar",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.smm4h-1.16/",
    pages = "104--109",
    abstract = "This paper details a system designed for Social Media Mining for Health Applications (SMM4H) Shared Task 2020. We specifically describe the systems designed to solve task 2: Automatic classification of multilingual tweets that report adverse effects, and task 3: Automatic extraction and normalization of adverse effects in English tweets. Fine tuning RoBERTa large for classifying English tweets enables us to achieve a F1 score of 56{\%}, which is an increase of +10{\%} compared to the average F1 score for all the submissions. Using BERT based NER and question answering, we are able to achieve a F1 score of 57.6{\%} for extracting adverse reaction mentions from tweets, which is an increase of +1.2{\%} compared to the average F1 score for all the submissions."
}