Using Knowledge-Embedded Attention to Augment Pre-trained Language Models for Fine-Grained Emotion Recognition

TL;DR

This paper introduces Knowledge-Embedded Attention (KEA), a method that enhances pre-trained language models with external emotion lexicon knowledge to improve fine-grained emotion recognition.

Contribution

The paper proposes KEA, a novel approach that integrates external emotion lexicon knowledge into pre-trained models like ELECTRA and BERT for better emotion differentiation.

Findings

Outperforms previous models on multiple datasets

Better distinguishes closely-confusable emotions

Enhances fine-grained emotion recognition accuracy

Abstract

Modern emotion recognition systems are trained to recognize only a small set of emotions, and hence fail to capture the broad spectrum of emotions people experience and express in daily life. In order to engage in more empathetic interactions, future AI has to perform \textit{fine-grained} emotion recognition, distinguishing between many more varied emotions. Here, we focus on improving fine-grained emotion recognition by introducing external knowledge into a pre-trained self-attention model. We propose Knowledge-Embedded Attention (KEA) to use knowledge from emotion lexicons to augment the contextual representations from pre-trained ELECTRA and BERT models. Our results and error analyses outperform previous models on several datasets, and is better able to differentiate closely-confusable emotions, such as afraid and terrified.