用加权损失缓解情绪识别中的数据不均衡问题
Addressing Data Imbalance in Transformer-Based Multi-Label Emotion Detection with Weighted Loss
- 通过动态调整类别权重，提升少数情绪类别的检测效果
- 在高频率情绪类上表现更好，但对少数类提升有限
- 适合需要快速部署的多标签情绪分析场景
本文研究了在SemEval-2025共享任务11中，基于Transformer模型的多标签情绪检测中简单加权损失函数的应用。针对数据不平衡问题，该方法通过动态调整类别权重，在不增加计算负担的前提下，提升了少数情绪类别的表现。我们在BRIGHTER数据集上评估了BERT、RoBERTa和BART模型，采用Micro F1、Macro F1、ROC-AUC、Accuracy及Jaccard相似系数等指标。结果表明，加权损失能有效提升高频情绪类的性能，但对少数情绪类改善有限。这些发现揭示了该方法在处理不平衡多标签情绪检测时的有效性与局限性。
原文摘要 · Abstract (English)
This paper explores the application of a simple weighted loss function to Transformer-based models for multi-label emotion detection in SemEval-2025 Shared Task 11. Our approach addresses data imbalance by dynamically adjusting class weights, thereby enhancing performance on minority emotion classes without the computational burden of traditional resampling methods. We evaluate BERT, RoBERTa, and BART on the BRIGHTER dataset, using evaluation metrics such as Micro F1, Macro F1, ROC-AUC, Accuracy, and Jaccard similarity coefficients. The results demonstrate that the weighted loss function improves performance on high-frequency emotion classes but shows limited impact on minority classes. These findings underscore both the effectiveness and the challenges of applying this approach to imbalanced multi-label emotion detection.
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