In traditional research approaches, sensory perception and emotion
classification have traditionally been considered separate domains. Yet, the
significant influence of sensory experiences on emotional responses is
undeniable. The natural language processing (NLP) community has often missed
the opportunity to merge sensory knowledge with emotion classification. To
address this gap, we propose SensoryT5, a neuro-cognitive approach that
integrates sensory information into the T5 (Text-to-Text Transfer Transformer)
model, designed specifically for fine-grained emotion classification. This
methodology incorporates sensory cues into the T5's attention mechanism,
enabling a harmonious balance between contextual understanding and sensory
awareness. The resulting model amplifies the richness of emotional
representations. In rigorous tests across various detailed emotion
classification datasets, SensoryT5 showcases improved performance, surpassing
both the foundational T5 model and current state-of-the-art works. Notably,
SensoryT5's success signifies a pivotal change in the NLP domain, highlighting
the potential influence of neuro-cognitive data in refining machine learning
models' emotional sensitivity.

传统研究方法中，感知和情感分类被认为是独立领域，然而，感官经验对情绪反应的重大影响是不可否认的。为了弥补这一差距，我们提出了 SensoryT5，这是一种将感官信息整合到 T5 模型中的神经认知方法，专门用于细粒度情感分类。这种方法将感官线索融入到 T5 的注意机制中，实现了上下文理解和感知意识之间的和谐平衡，从而增强了情感表达的丰富性。在各种详细情感分类数据集的严格测试中，SensoryT5 展示了改进的性能，超过了 T5 模型和当前最先进的作品。值得注意的是，SensoryT5 的成功标志着自然语言处理领域的重大变革，凸显了神经认知数据在改进机器学习模型的情感敏感性方面的潜在影响。