  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.
