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.

翻译：跨语言情感检测使我们能够大规模分析全球趋势、公众舆论和社会现象。我们参与了可解释性跨语言情感检测（EXALT）共享任务，在情感检测子任务的评估集上取得了0.6046的F1分数。我们的系统以超过0.16的绝对F1分数优势优于基线模型，并在参赛系统中位列第二。我们采用基于大语言模型的微调、零样本学习和少样本学习方法，以及基于嵌入的非大语言模型技术（BiLSTM和KNN）进行了实验。此外，我们提出了两种创新方法：多轮迭代智能体工作流与多二元分类器智能体工作流。研究发现，基于大语言模型的方法在多语言情感检测任务中表现出良好性能。进一步地，将所有实验模型集成的组合方法相比单一方法获得了更高的F1分数。