@inproceedings{xia-etal-2024-sensoryt5,
    title = "{S}ensory{T}5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification",
    author = "Xia, Yuhan  and
      Zhao, Qingqing  and
      Long, Yunfei  and
      Xu, Ge  and
      Wang, Jia",
    editor = "Zock, Michael  and
      Chersoni, Emmanuele  and
      Hsu, Yu-Yin  and
      de Deyne, Simon",
    booktitle = "Proceedings of the Workshop on Cognitive Aspects of the Lexicon @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.cogalex-1.19",
    pages = "162--174",
    abstract = "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 neurocognitive 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 neurocognitive data in refining machine learning models{'} emotional sensitivity.",
}

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 neurocognitive 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 neurocognitive data in refining machine learning models{'} emotional sensitivity.
Xia, Yuhan Zhao, Qingqing Long, Yunfei Xu, Ge Wang, Jia
SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification