  This paper describes the approach to the Emotion Classification shared task
held at WASSA 2022 by team PVGs AI Club. This Track 2 sub-task focuses on
building models which can predict a multi-class emotion label based on essays
from news articles where a person, group or another entity is affected.
Baseline transformer models have been demonstrating good results on sequence
classification tasks, and we aim to improve this performance with the help of
ensembling techniques, and by leveraging two variations of emotion-specific
representations. We observe better results than our baseline models and achieve
an accuracy of 0.619 and a macro F1 score of 0.520 on the emotion
classification task.
