Multimodal Language Understanding and Data Governance: Exploring Advanced NLP in Sentiment and Sign Language Interpretation under Evolving Privacy Regulations
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Abstract
The rapid evolution of multimodal language understanding has redefined how artificial intelligence interprets human communication, integrating visual, auditory, and textual cues for more inclusive and intelligent systems. This study explores the convergence of advanced Natural Language Processing (NLP), sentiment analysis, and sign language interpretation within the framework of modern data governance and privacy regulations. The proposed framework leverages transformer-based multimodal architectures—combining textual embeddings with visual-spatial gesture representations—to enhance emotional comprehension and contextual sensitivity in real-time human–AI interaction. In parallel, the research addresses challenges of data integrity, bias mitigation, and ethical compliance under evolving global privacy standards such as GDPR, CCPA, and emerging AI Act directives. By embedding privacy-aware learning mechanisms and federated data models, the system ensures secure, decentralized model training without compromising accuracy or accessibility. The findings highlight the transformative potential of integrating multimodal NLP and compliant data governance in creating empathetic, transparent, and responsible AI-driven communication systems that support inclusive interaction across linguistic and sensory boundaries.
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