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

2021/01/30 by Guda, Bhanu Prakash Reddy, Garimella, Aparna, Chhaya, Niyati · 3 citations
#Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2102.00272

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 to predict 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.

Cited by

• Empathy Level Prediction in Multi-Modal Scenario with Supervisory Documentation Assistance
• Toward Human-Centered Readability Evaluation
• Words Like Knives: Backstory-Personalized Modeling and Detection of Violent Communication

Related

• Modeling Empathy and Distress in Reaction to News Stories
• PVG at WASSA 2021: A Multi-Input, Multi-Task, Transformer-Based Architecture for Empathy and Distress Prediction
• Empathic Conversations: A Multi-level Dataset of Contextualized Conversations
• SENSE-7: Taxonomy and Dataset for Measuring User Perceptions of Empathy in Sustained Human-AI Conversations
• APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation