Multi-Task Learning to Capture Changes in Mood Over Time
Prasadith Kirinde Gamaarachchige, Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Diana Inkpen
Abstract
This paper investigates the impact of using Multi-Task Learning (MTL) to predict mood changes over time for each individual (social media user). The presented models were developed as a part of the Computational Linguistics and Clinical Psychology (CLPsych) 2022 shared task. Given the limited number of Reddit social media users, as well as their posts, we decided to experiment with different multi-task learning architectures to identify to what extent knowledge can be shared among similar tasks. Due to class imbalance at both post and user levels and to accommodate task alignment, we randomly sampled an equal number of instances from the respective classes and performed ensemble learning to reduce prediction variance. Faced with several constraints, we managed to produce competitive results that could provide insights into the use of multi-task learning to identify mood changes over time and suicide ideation risk.- Anthology ID:
- 2022.clpsych-1.22
- Volume:
- Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, USA
- Venue:
- CLPsych
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 232–238
- Language:
- URL:
- https://aclanthology.org/2022.clpsych-1.22
- DOI:
- 10.18653/v1/2022.clpsych-1.22
- Cite (ACL):
- Prasadith Kirinde Gamaarachchige, Ahmed Husseini Orabi, Mahmoud Husseini Orabi, and Diana Inkpen. 2022. Multi-Task Learning to Capture Changes in Mood Over Time. In Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology, pages 232–238, Seattle, USA. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Task Learning to Capture Changes in Mood Over Time (Kirinde Gamaarachchige et al., CLPsych 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.clpsych-1.22.pdf
- Data
- SMHD