@inproceedings{lin-etal-2023-ncuee,
title = "{NCUEE}-{NLP} at {WASSA} 2023 Shared Task 1: Empathy and Emotion Prediction Using Sentiment-Enhanced {R}o{BERT}a Transformers",
author = "Lin, Tzu-Mi and
Chang, Jung-Ying and
Lee, Lung-Hao",
editor = "Barnes, Jeremy and
De Clercq, Orph{\'e}e and
Klinger, Roman",
booktitle = "Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wassa-1.49/",
doi = "10.18653/v1/2023.wassa-1.49",
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."
}
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%0 Conference Proceedings
%T NCUEE-NLP at WASSA 2023 Shared Task 1: Empathy and Emotion Prediction Using Sentiment-Enhanced RoBERTa Transformers
%A Lin, Tzu-Mi
%A Chang, Jung-Ying
%A Lee, Lung-Hao
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Klinger, Roman
%S Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F lin-etal-2023-ncuee
%X 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.
%R 10.18653/v1/2023.wassa-1.49
%U https://aclanthology.org/2023.wassa-1.49/
%U https://doi.org/10.18653/v1/2023.wassa-1.49
%P 548-552
Markdown (Informal)
[NCUEE-NLP at WASSA 2023 Shared Task 1: Empathy and Emotion Prediction Using Sentiment-Enhanced RoBERTa Transformers](https://aclanthology.org/2023.wassa-1.49/) (Lin et al., WASSA 2023)
- NCUEE-NLP at WASSA 2023 Shared Task 1: Empathy and Emotion Prediction Using Sentiment-Enhanced RoBERTa Transformers (Lin et al., WASSA 2023)
ACL
- Tzu-Mi Lin, Jung-Ying Chang, and Lung-Hao Lee. 2023. NCUEE-NLP at WASSA 2023 Shared Task 1: Empathy and Emotion Prediction Using Sentiment-Enhanced RoBERTa Transformers. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 548–552, Toronto, Canada. Association for Computational Linguistics.