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 · 2024
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 F1score 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 BiL-STM 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 F1scores than any single approach alone.