用连续情绪标签提升模型对细微情感的捕捉能力。
Continuous Adversarial Text Representation Learning for Affective Recognition
- 引入连续唤醒-效价标签指导对比学习，捕捉多维情感细节。
- 通过梯度敏感性动态扰动关键词，增强情感线索响应能力。
- 在情感分类上提升15.5%，适合需要精准情绪理解的应用。
尽管预训练语言模型在语义理解方面表现优异，但在捕捉影响情感识别任务的关键细微情感信息方面仍存在不足。为此，我们提出一种新框架，用于增强基于Transformer模型的情感感知嵌入。该方法引入连续效价-唤醒标签系统，指导对比学习，更有效地捕捉微妙且多维度的情感特征。此外，采用基于梯度显著性的动态标记扰动机制，聚焦于与情感相关的标记，提升模型对情感线索的敏感性。实验结果表明，所提框架在情感分类基准上优于现有方法，最高提升达15.5%，凸显连续标签的重要性。该成果验证了框架在情感表征学习中的有效性，支持精确且上下文相关的感情理解。
原文摘要 · Abstract (English)
While pre-trained language models excel at semantic understanding, they often struggle to capture nuanced affective information critical for affective recognition tasks. To address these limitations, we propose a novel framework for enhancing emotion-aware embeddings in transformer-based models. Our approach introduces a continuous valence-arousal labeling system to guide contrastive learning, which captures subtle and multi-dimensional emotional nuances more effectively. Furthermore, we employ a dynamic token perturbation mechanism, using gradient-based saliency to focus on sentiment-relevant tokens, improving model sensitivity to emotional cues. The experimental results demonstrate that the proposed framework outperforms existing methods, achieving up to 15.5% improvement in the emotion classification benchmark, highlighting the importance of employing continuous labels. This improvement demonstrates that the proposed framework is effective in affective representation learning and enables precise and contextually relevant emotional understanding.
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