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Emotion-Infused Models for Explainable Psychological Stress Detection

2021-06-01NAACL 2021Code Available0· sign in to hype

Elsbeth Turcan, Smaranda Muresan, Kathleen McKeown

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Abstract

The problem of detecting psychological stress in online posts, and more broadly, of detecting people in distress or in need of help, is a sensitive application for which the ability to interpret models is vital. Here, we present work exploring the use of a semantically related task, emotion detection, for equally competent but more explainable and human-like psychological stress detection as compared to a black-box model. In particular, we explore the use of multi-task learning as well as emotion-based language model fine-tuning. With our emotion-infused models, we see comparable results to state-of-the-art BERT. Our analysis of the words used for prediction show that our emotion-infused models mirror psychological components of stress.

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