Abstract
We present a novel deep learning-based framework to generate embedding representations of fine-grained emotions that can be used to computationally describe psychological models of emotions. Our framework integrates a contextualized embedding encoder with a multi-head probing model that enables to interpret dynamically learned representations optimized for an emotion classification task. Our model is evaluated on the Empathetic Dialogue dataset and shows the state-of-the-art result for classifying 32 emotions. Our layer analysis can derive an emotion graph to depict hierarchical relations among the emotions. Our emotion representations can be used to generate an emotion wheel directly comparable to the one from Plutchik’s model, and also augment the values of missing emotions in the PAD emotional state model.- Anthology ID:
- 2021.cmcl-1.18
- Volume:
- Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Emmanuele Chersoni, Nora Hollenstein, Cassandra Jacobs, Yohei Oseki, Laurent Prévot, Enrico Santus
- Venue:
- CMCL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 141–148
- Language:
- URL:
- https://aclanthology.org/2021.cmcl-1.18
- DOI:
- 10.18653/v1/2021.cmcl-1.18
- Cite (ACL):
- Yuting Guo and Jinho D. Choi. 2021. Enhancing Cognitive Models of Emotions with Representation Learning. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 141–148, Online. Association for Computational Linguistics.
- Cite (Informal):
- Enhancing Cognitive Models of Emotions with Representation Learning (Guo & Choi, CMCL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2021.cmcl-1.18.pdf
- Code
- emorynlp/CMCL-2021