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.- Anthology ID:
- 2021.naacl-main.230
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2895–2909
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.230
- DOI:
- 10.18653/v1/2021.naacl-main.230
- Cite (ACL):
- Elsbeth Turcan, Smaranda Muresan, and Kathleen McKeown. 2021. Emotion-Infused Models for Explainable Psychological Stress Detection. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2895–2909, Online. Association for Computational Linguistics.
- Cite (Informal):
- Emotion-Infused Models for Explainable Psychological Stress Detection (Turcan et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.naacl-main.230.pdf
- Code
- eturcan/emotion-infused
- Data
- Dreaddit