@inproceedings{lin-etal-2024-nycu,
title = "{NYCU}-{NLP} at {EXALT} 2024: Assembling Large Language Models for Cross-Lingual Emotion and Trigger Detection",
author = "Lin, Tzu-Mi and
Xu, Zhe-Yu and
Zhou, Jian-Yu and
Lee, Lung-Hao",
editor = "De Clercq, Orph{\'e}e and
Barriere, Valentin and
Barnes, Jeremy and
Klinger, Roman and
Sedoc, Jo{\~a}o and
Tafreshi, Shabnam",
booktitle = "Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.wassa-1.50",
pages = "505--510",
abstract = "This study describes the model design of the NYCU-NLP system for the EXALT shared task at the WASSA 2024 workshop. We instruction-tune several large language models and then assemble various model combinations as our main system architecture for cross-lingual emotion and trigger detection in tweets. Experimental results showed that our best performing submission is an assembly of the Starling (7B) and Llama 3 (8B) models. Our submission was ranked sixth of 17 participating systems for the emotion detection subtask, and fifth of 7 systems for the binary trigger detection subtask.",
}
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<abstract>This study describes the model design of the NYCU-NLP system for the EXALT shared task at the WASSA 2024 workshop. We instruction-tune several large language models and then assemble various model combinations as our main system architecture for cross-lingual emotion and trigger detection in tweets. Experimental results showed that our best performing submission is an assembly of the Starling (7B) and Llama 3 (8B) models. Our submission was ranked sixth of 17 participating systems for the emotion detection subtask, and fifth of 7 systems for the binary trigger detection subtask.</abstract>
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%0 Conference Proceedings
%T NYCU-NLP at EXALT 2024: Assembling Large Language Models for Cross-Lingual Emotion and Trigger Detection
%A Lin, Tzu-Mi
%A Xu, Zhe-Yu
%A Zhou, Jian-Yu
%A Lee, Lung-Hao
%Y De Clercq, Orphée
%Y Barriere, Valentin
%Y Barnes, Jeremy
%Y Klinger, Roman
%Y Sedoc, João
%Y Tafreshi, Shabnam
%S Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F lin-etal-2024-nycu
%X This study describes the model design of the NYCU-NLP system for the EXALT shared task at the WASSA 2024 workshop. We instruction-tune several large language models and then assemble various model combinations as our main system architecture for cross-lingual emotion and trigger detection in tweets. Experimental results showed that our best performing submission is an assembly of the Starling (7B) and Llama 3 (8B) models. Our submission was ranked sixth of 17 participating systems for the emotion detection subtask, and fifth of 7 systems for the binary trigger detection subtask.
%U https://aclanthology.org/2024.wassa-1.50
%P 505-510
Markdown (Informal)
[NYCU-NLP at EXALT 2024: Assembling Large Language Models for Cross-Lingual Emotion and Trigger Detection](https://aclanthology.org/2024.wassa-1.50) (Lin et al., WASSA-WS 2024)
ACL