@inproceedings{rinaldi-etal-2020-predicting,
    title = "Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts",
    author = "Rinaldi, Alex  and
      Fox Tree, Jean  and
      Chaturvedi, Snigdha",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.2",
    doi = "10.18653/v1/2020.acl-main.2",
    pages = "7--18",
    abstract = "Accurately diagnosing depression is difficult{--} requiring time-intensive interviews, assessments, and analysis. Hence, automated methods that can assess linguistic patterns in these interviews could help psychiatric professionals make faster, more informed decisions about diagnosis. We propose JLPC, a model that analyzes interview transcripts to identify depression while jointly categorizing interview prompts into latent categories. This latent categorization allows the model to define high-level conversational contexts that influence patterns of language in depressed individuals. We show that the proposed model not only outperforms competitive baselines, but that its latent prompt categories provide psycholinguistic insights about depression.",
}