Empathy and Distress Prediction using Transformer Multi-output Regression and Emotion Analysis with an Ensemble of Supervised and Zero-Shot Learning Models

Flor Miriam Del Arco, Jaime Collado-Montañez, L. Alfonso Ureña, María-Teresa Martín-Valdivia


Abstract
This paper describes the participation of the SINAI research group at WASSA 2022 (Empathy and Personality Detection and Emotion Classification). Specifically, we participate in Track 1 (Empathy and Distress predictions) and Track 2 (Emotion classification). We conducted extensive experiments developing different machine learning solutions in line with the state of the art in Natural Language Processing. For Track 1, a Transformer multi-output regression model is proposed. For Track 2, we aim to explore recent techniques based on Zero-Shot Learning models including a Natural Language Inference model and GPT-3, using them in an ensemble manner with a fine-tune RoBERTa model. Our team ranked 2nd in the first track and 3rd in the second track.
Anthology ID:
2022.wassa-1.23
Volume:
Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
239–244
Language:
URL:
https://aclanthology.org/2022.wassa-1.23
DOI:
10.18653/v1/2022.wassa-1.23
Bibkey:
Cite (ACL):
Flor Miriam Del Arco, Jaime Collado-Montañez, L. Alfonso Ureña, and María-Teresa Martín-Valdivia. 2022. Empathy and Distress Prediction using Transformer Multi-output Regression and Emotion Analysis with an Ensemble of Supervised and Zero-Shot Learning Models. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pages 239–244, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Empathy and Distress Prediction using Transformer Multi-output Regression and Emotion Analysis with an Ensemble of Supervised and Zero-Shot Learning Models (Del Arco et al., WASSA 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.wassa-1.23.pdf
Data
CARER