TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion Detection

Long Cheng, Qihao Shao, Christine Zhao, Sheng Bi, Gina-Anne Levow


Abstract
Cross-lingual emotion detection allows us to analyze global trends, public opinion, and social phenomena at scale. We participated in the Explainability of Cross-lingual Emotion Detection (EXALT) shared task, achieving an F1-score of 0.6046 on the evaluation set for the emotion detection sub-task. Our system outperformed the baseline by more than 0.16 F1-score absolute, and ranked second amongst competing systems. We conducted experiments using fine-tuning, zero-shot learning, and few-shot learning for Large Language Model (LLM)-based models as well as embedding-based BiLSTM and KNN for non-LLM-based techniques. Additionally, we introduced two novel methods: the Multi-Iteration Agentic Workflow and the Multi-Binary-Classifier Agentic Workflow. We found that LLM-based approaches provided good performance on multilingual emotion detection. Furthermore, ensembles combining all our experimented models yielded higher F1-scores than any single approach alone.
Anthology ID:
2024.wassa-1.49
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:
495–504
Language:
URL:
https://aclanthology.org/2024.wassa-1.49
DOI:
Bibkey:
Cite (ACL):
Long Cheng, Qihao Shao, Christine Zhao, Sheng Bi, and Gina-Anne Levow. 2024. TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion Detection. In Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 495–504, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion Detection (Cheng et al., WASSA-WS 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-5/2024.wassa-1.49.pdf