Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviews

• URL: http://arxiv.org/abs/2006.08336v1
• Date: Thu, 4 Jun 2020 20:55:22 GMT
• Title: Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviews
• Authors: D. Xezonaki, G. Paraskevopoulos, A. Potamianos, S. Narayanan
• Abstract summary: Depression is a mental disorder that impacts not only the subject's mood but also the use of language. We use a Hierarchical Attention Network to classify interviews of depressed subjects. We augment the attention layer of our model with a conditioning mechanism on linguistic features, extracted from affective lexica.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work we propose a machine learning model for depression detection from transcribed clinical interviews. Depression is a mental disorder that impacts not only the subject's mood but also the use of language. To this end we use a Hierarchical Attention Network to classify interviews of depressed subjects. We augment the attention layer of our model with a conditioning mechanism on linguistic features, extracted from affective lexica. Our analysis shows that individuals diagnosed with depression use affective language to a greater extent than not-depressed. Our experiments show that external affective information improves the performance of the proposed architecture in the General Psychotherapy Corpus and the DAIC-WoZ 2017 depression datasets, achieving state-of-the-art 71.6 and 68.6 F1 scores respectively.

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