Transformer-based Architecture for Empathy Prediction and Emotion Classification

AbstractThis paper describes the contribution of team PHG to the WASSA 2022 shared task on Empathy Prediction and Emotion Classification. The broad goal of this task was to model an empathy score, a distress score and the type of emotion associated with the person who had reacted to the essay written in response to a newspaper article. We have used the RoBERTa model for training and top of which few layers are added to finetune the transformer. We also use few machine learning techniques to augment as well as upsample the data. Our system achieves a Pearson Correlation Coefficient of 0.488 on Task 1 (Empathy - 0.470 and Distress - 0.506) and Macro F1-score of 0.531 on Task 2.