DAIC-WOZ: On the Validity of Using the Therapist's prompts in Automatic Depression Detection from Clinical Interviews
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0196771" target="_blank" >RIV/00216305:26230/26:0196771 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2024.clinicalnlp-1.8/" target="_blank" >https://aclanthology.org/2024.clinicalnlp-1.8/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18653/v1/2024.clinicalnlp-1.8" target="_blank" >10.18653/v1/2024.clinicalnlp-1.8</a>
Alternative languages
Result language
angličtina
Original language name
DAIC-WOZ: On the Validity of Using the Therapist's prompts in Automatic Depression Detection from Clinical Interviews
Original language description
Automatic depression detection from conversational data has gained significant interest in recent years. The DAIC-WOZ dataset, interviews conducted by a human-controlled virtual agent, has been widely used for this task. Recent studies have reported enhanced performance when incorporating interviewer's prompts into the model. In this work, we hypothesize that this improvement might be mainly due to a bias present in these prompts, rather than the proposed architectures and methods. Through ablation experiments and qualitative analysis, we discover that models using interviewer's prompts learn to focus on a specific region of the interviews, where questions about past experiences with mental health issues are asked, and use them as discriminative shortcuts to detect depressed participants. In contrast, models using participant responses gather evidence from across the entire interview. Finally, to highlight the magnitude of this bias, we achieve a 0.90 F1 score by intentionally exploiting it, the highest result reported to date on this dataset using only textual information. Our findings underline the need for caution when incorporating interviewers' prompts into models, as they may inadvertently learn to exploit targeted prompts, rather than learning to characterize the language and behavior that are genuinely indicative of the patient's mental health condition.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 6th Clinical Natural Language Processing Workshop, ClinicalNLP@NAACL
ISBN
979-8-89176-109-4
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
82-90
Publisher name
Association for Computational Linguistics
Place of publication
Mexico City
Event location
Mexico City
Event date
Jun 21, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001606850700008