  We focus on exploring various approaches of Zero-Shot Learning (ZSL) and
their explainability for a challenging yet important supervised learning task
notorious for training data scarcity, i.e. Depression Symptoms Detection (DSD)
from text. We start with a comprehensive synthesis of different components of
our ZSL modeling and analysis of our ground truth samples and Depression
symptom clues curation process with the help of a practicing clinician. We next
analyze the accuracy of various state-of-the-art ZSL models and their potential
enhancements for our task. Further, we sketch a framework for the use of ZSL
for hierarchical text-based explanation mechanism, which we call, Syntax
Tree-Guided Semantic Explanation (STEP). Finally, we summarize experiments from
which we conclude that we can use ZSL models and achieve reasonable accuracy
and explainability, measured by a proposed Explainability Index (EI). This work
is, to our knowledge, the first work to exhaustively explore the efficacy of
ZSL models for DSD task, both in terms of accuracy and explainability.
