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

arXiv: https://arxiv.org/abs/2106.10928 · HTML: https://mlsanity.com/paper/2106.10928/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media](https://mlsanity.com/paper/1710.05429/) (2017)
- [A multi-component framework for the analysis and design of explainable artificial intelligence](https://mlsanity.com/paper/2005.01908/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions](https://mlsanity.com/paper/2205.03235/) (2022)

## Abstract (arXiv)

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.