SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification

TL;DR

SensoryT5 is a novel NLP model that integrates sensory information into T5 to improve fine-grained emotion classification, demonstrating enhanced performance by combining sensory cues with contextual understanding.

Contribution

This work introduces SensoryT5, the first model to incorporate sensory knowledge into T5 for emotion classification, bridging sensory perception and NLP.

Findings

Outperforms baseline T5 and state-of-the-art models on emotion datasets.

Effectively integrates sensory cues into attention mechanisms.

Enhances emotional representation richness.

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 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.