  Based on the WASSA 2022 Shared Task on Empathy Detection and Emotion
Classification, we predict the level of empathic concern and personal distress
displayed in essays. For the first stage of this project we implemented a
Feed-Forward Neural Network using sentence-level embeddings as features. We
experimented with four different embedding models for generating the inputs to
the neural network. The subsequent stage builds upon the previous work and we
have implemented three types of revisions. The first revision focuses on the
enhancements to the model architecture and the training approach. The second
revision focuses on handling class imbalance using stratified data sampling.
The third revision focuses on leveraging lexical resources, where we apply four
different resources to enrich the features associated with the dataset. During
the final stage of this project, we have created the final end-to-end system
for the primary task using an ensemble of models to revise primary task
performance. Additionally, as part of the final stage, these approaches have
been adapted to the WASSA 2023 Shared Task on Empathy Emotion and Personality
Detection in Interactions, in which the empathic concern, emotion polarity, and
emotion intensity in dyadic text conversations are predicted.
