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

STEP-EZ: Syntax Tree guided semantic ExPlanation for Explainable Zero-shot modeling of clinical depression symptoms from text