EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Prediction

Bhanu Prakash Reddy Guda, Aparna Garimella, Niyati Chhaya


Abstract
Affect preferences vary with user demographics, and tapping into demographic information provides important cues about the users’ language preferences. In this paper, we utilize the user demographics and propose EmpathBERT, a demographic-aware framework for empathy prediction based on BERT. Through several comparative experiments, we show that EmpathBERT surpasses traditional machine learning and deep learning models, and illustrate the importance of user demographics, for predicting empathy and distress in user responses to stimulative news articles. We also highlight the importance of affect information in the responses by developing affect-aware models to predict user demographic attributes.
Anthology ID:
2021.eacl-main.268
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3072–3079
Language:
URL:
https://aclanthology.org/2021.eacl-main.268
DOI:
10.18653/v1/2021.eacl-main.268
Bibkey:
Cite (ACL):
Bhanu Prakash Reddy Guda, Aparna Garimella, and Niyati Chhaya. 2021. EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Prediction. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3072–3079, Online. Association for Computational Linguistics.
Cite (Informal):
EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Prediction (Guda et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.268.pdf