Empathy and Distress Detection using Ensembles of Transformer Models
Tanmay Chavan Kshitij Deshpande Sheetal Sonawane
This paper presents our approach for the WASSA 2023 Empathy, Emotion and
Personality Shared Task. Empathy and distress are human feelings that are
implicitly expressed in natural discourses. Empathy and distress detection are
crucial challenges in Natural Language Processing that can aid our
understanding of conversations. The provided dataset consists of several
long-text examples in the English language, with each example associated with a
numeric score for empathy and distress. We experiment with several BERT-based
models as a part of our approach. We also try various ensemble methods. Our
final submission has a Pearson's r score of 0.346, placing us third in the
empathy and distress detection subtask.