  Depression is a serious mental illness that impacts the way people
communicate, especially through their emotions, and, allegedly, the way they
interact with others. This work examines depression signals in dialogs, a less
studied setting that suffers from data sparsity. We hypothesize that depression
and emotion can inform each other, and we propose to explore the influence of
dialog structure through topic and dialog act prediction. We investigate a
Multi-Task Learning (MTL) approach, where all tasks mentioned above are learned
jointly with dialog-tailored hierarchical modeling. We experiment on the DAIC
and DailyDialog corpora-both contain dialogs in English-and show important
improvements over state-ofthe-art on depression detection (at best 70.6% F 1),
which demonstrates the correlation of depression with emotion and dialog
organization and the power of MTL to leverage information from different
sources.
