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.- Anthology ID:
- 2024.wassa-1.50
- Volume:
- Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Orphée De Clercq, Valentin Barriere, Jeremy Barnes, Roman Klinger, João Sedoc, Shabnam Tafreshi
- Venues:
- WASSA | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 505–510
- Language:
- URL:
- https://aclanthology.org/2024.wassa-1.50
- DOI:
- Cite (ACL):
- Tzu-Mi Lin, Zhe-Yu Xu, Jian-Yu Zhou, and Lung-Hao Lee. 2024. NYCU-NLP at EXALT 2024: Assembling Large Language Models for Cross-Lingual Emotion and Trigger Detection. In Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 505–510, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- NYCU-NLP at EXALT 2024: Assembling Large Language Models for Cross-Lingual Emotion and Trigger Detection (Lin et al., WASSA-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.wassa-1.50.pdf