Tzu-Mi Lin, Jung-Ying Chang, and Lung-Hao Lee.
In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA’23), pages 548-552.
Abstract
This paper describes our proposed system design for the WASSA 2023 shared task 1. We propose a unified architecture of ensemble neural networks to integrate the original RoBERTa transformer with two sentiment-enhanced RoBERTa-Twitter and EmoBERTa models. For Track 1 at the speech-turn level, our best submission achieved an average Pearson correlation score of 0.7236, ranking fourth for empathy, emotion polarity and emotion intensity prediction. For Track 2 at the essay-level, our best submission obtained an average Pearson correlation score of 0.4178 for predicting empathy and distress scores, ranked first among all nine submissions.
