@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification
%A Xia, Yuhan
%A Zhao, Qingqing
%A Long, Yunfei
%A Xu, Ge
%A Wang, Jia
%Y Zock, Michael
%Y Chersoni, Emmanuele
%Y Hsu, Yu-Yin
%Y de Deyne, Simon
%S Proceedings of the Workshop on Cognitive Aspects of the Lexicon @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F xia-etal-2024-sensoryt5
%X 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.
%U https://aclanthology.org/2024.cogalex-1.19
%P 162-174
Markdown (Informal)
[SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification](https://aclanthology.org/2024.cogalex-1.19) (Xia et al., CogALex 2024)
ACL