用Llama-3生成解释性内容，提升RoBERTa多标签情绪分类效果
Lotus at SemEval-2025 Task 11: RoBERTa with Llama-3 Generated Explanations for Multi-Label Emotion Classification
- 用Llama-3生成解释文本，辅助RoBERTa理解模糊情绪表达
- 在恐惧、快乐、悲伤等情绪上F1分数显著提升
- 适合需要可解释情绪分析的NLP应用
本文提出一种新型多标签情绪检测方法，利用Llama-3生成解释性内容以澄清模糊情绪表达，从而提升RoBERTa的情绪分类性能。通过引入解释性上下文，该方法在恐惧、喜悦和悲伤等情绪类别上显著改善了F1分数，优于仅依赖文本的模型。解释内容有助于化解歧义，应对重叠情绪线索等挑战，推动多标签情绪分类任务的重要进展。
原文摘要 · Abstract (English)
This paper presents a novel approach for multi-label emotion detection, where Llama-3 is used to generate explanatory content that clarifies ambiguous emotional expressions, thereby enhancing RoBERTa's emotion classification performance. By incorporating explanatory context, our method improves F1-scores, particularly for emotions like fear, joy, and sadness, and outperforms text-only models. The addition of explanatory content helps resolve ambiguity, addresses challenges like overlapping emotional cues, and enhances multi-label classification, marking a significant advancement in emotion detection tasks.
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