@inproceedings{singh-motlicek-2022-idiap-submission-lt,
    title = "{IDIAP} Submission@{LT}-{EDI}-{ACL}2022: Detecting Signs of Depression from Social Media Text",
    author = "Singh, Muskaan  and
      Motlicek, Petr",
    editor = "Chakravarthi, Bharathi Raja  and
      Bharathi, B  and
      McCrae, John P  and
      Zarrouk, Manel  and
      Bali, Kalika  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.ltedi-1.56/",
    doi = "10.18653/v1/2022.ltedi-1.56",
    pages = "362--368",
    abstract = "Depression is a common illness involving sadness and lack of interest in all day-to-day activities. It is important to detect depression at an early stage as it is treated at an early stage to avoid consequences. In this paper, we present our system submission of ARGUABLY for DepSign-LT-EDI@ACL-2022. We aim to detect the signs of depression of a person from their social media postings wherein people share their feelings and emotions. The proposed system is an ensembled voting model with fine-tuned BERT, RoBERTa, and XLNet. Given social media postings in English, the submitted system classify the signs of depression into three labels, namely ``not depressed,'' ``moderately depressed,'' and ``severely depressed.'' Our best model is ranked $3^{rd}$ position with 0.54{\%} accuracy . We make our codebase accessible here."
}