@inproceedings{xherija-choi-2022-complx,
    title = "{C}omp{L}x@{SMM}4{H}{'}22: In-domain pretrained language models for detection of adverse drug reaction mentions in {E}nglish tweets",
    author = "Xherija, Orest  and
      Choi, Hojoon",
    editor = "Gonzalez-Hernandez, Graciela  and
      Weissenbacher, Davy",
    booktitle = "Proceedings of the Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.smm4h-1.47/",
    pages = "176--181",
    abstract = "The paper describes the system that team CompLx developed for sub-task 1a of the Social Media Mining for Health 2022 ({\#}SMM4H) Shared Task. We finetune a RoBERTa model, a pretrained, transformer-based language model, on a provided dataset to classify English tweets for mentions of Adverse Drug Reactions (ADRs), i.e. negative side effects related to medication intake. With only a simple finetuning, our approach achieves competitive results, significantly outperforming the average score across submitted systems. We make the model checkpoints and code publicly available. We also create a web application to provide a user-friendly, readily accessible interface for anyone interested in exploring the model{'}s capabilities."
}