@inproceedings{morales-etal-2021-team,
    title = "Team 9: A Comparison of Simple vs. Complex Models for Suicide Risk Assessment",
    author = "Morales, Michelle  and
      Dey, Prajjalita  and
      Kohli, Kriti",
    editor = "Goharian, Nazli  and
      Resnik, Philip  and
      Yates, Andrew  and
      Ireland, Molly  and
      Niederhoffer, Kate  and
      Resnik, Rebecca",
    booktitle = "Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.clpsych-1.11/",
    doi = "10.18653/v1/2021.clpsych-1.11",
    pages = "99--102",
    abstract = "This work presents the systems explored as part of the CLPsych 2021 Shared Task. More specifically, this work explores the relative performance of models trained on social me- dia data for suicide risk assessment. For this task, we aim to investigate whether or not simple traditional models can outperform more complex fine-tuned deep learning mod- els. Specifically, we build and compare a range of models including simple baseline models, feature-engineered machine learning models, and lastly, fine-tuned deep learning models. We find that simple more traditional machine learning models are more suited for this task and highlight the challenges faced when trying to leverage more sophisticated deep learning models."
}