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

EACL 2021 · Bhanu Prakash Reddy Guda, Aparna Garimella, Niyati Chhaya ·

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.

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BIG-bench Machine Learning

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Adam • Attention Dropout • BERT • Dense Connections • Dropout • GELU • Layer Normalization • Linear Layer • Linear Warmup With Linear Decay • Multi-Head Attention • Residual Connection • Scaled Dot-Product Attention • Softmax • Weight Decay • WordPiece

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

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