WASSA@IITK at WASSA 2021: Multi-task Learning and Transformer Finetuning for Emotion Classification and Empathy Prediction

TL;DR

This paper presents a multi-task learning approach using ELECTRA and ensembling techniques for emotion classification and empathy prediction, achieving top rankings in the WASSA 2021 shared task.

Contribution

It introduces a novel combination of ELECTRA-based deep learning and multi-task learning for improved emotion and empathy prediction.

Findings

Achieved a Pearson correlation of 0.533 on empathy prediction

Obtained a macro F1 score of 0.5528 on emotion classification

Ranked 1st in emotion classification and 3rd in empathy prediction

Abstract

This paper describes our contribution to the WASSA 2021 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 overall level of emotion of an essay written in response to a newspaper article associated with harm to someone. We have used the ELECTRA model abundantly and also advanced deep learning approaches like multi-task learning. Additionally, we also leveraged standard machine learning techniques like ensembling. Our system achieves a Pearson Correlation Coefficient of 0.533 on sub-task I and a macro F1 score of 0.5528 on sub-task II. We ranked 1st in Emotion Classification sub-task and 3rd in Empathy Prediction sub-task