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.
Cite
CITATION STYLE
Lin, T. M., Chang, J. Y., & Lee, L. H. (2023). NCUEE-NLP at WASSA 2023 Empathy, Emotion, and Personality Shared Task: Perceived Intensity Prediction Using Sentiment-Enhanced RoBERTa Transformers. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 548–552). Association for Computational Linguistics (ACL).
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