Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts

Alex Rinaldi, Jean Fox Tree, Snigdha Chaturvedi


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.
Anthology ID:
2020.acl-main.2
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7–18
Language:
URL:
https://aclanthology.org/2020.acl-main.2
DOI:
10.18653/v1/2020.acl-main.2
Bibkey:
Cite (ACL):
Alex Rinaldi, Jean Fox Tree, and Snigdha Chaturvedi. 2020. Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7–18, Online. Association for Computational Linguistics.
Cite (Informal):
Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts (Rinaldi et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.2.pdf
Video:
 http://slideslive.com/38928967