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.

我们参与了Explainability of Cross-lingual Emotion Detection（EXALT）共享任务，在情感检测子任务的评估集上实现了0.6046的F1分数，超过基准模型0.16个F1分数，并在竞争系统中排名第二。我们通过使用Fine-tuning、Zero-shot learning和Few-shot learning方法在基于Large Language Model（LLM）和非LLM的技术中，包括嵌入式的BiLSTM和KNN进行实验。此外，我们引入了两种新方法：多次迭代代理工作流和多二元分类器代理工作流。我们发现LLM方法对多语言情感检测提供了良好的性能。此外，将所有实验模型组合起来的集成模型比任何单一方法都有更高的F1分数。

TEII: 跨语言情感检测中的思考、解释、交互和迭代