Computational Overhead and Latency Trade-offs in Graph-Based Fusion versus Token Redundancy Reduction for Cross-Document NLI

Abstract

Abstract: Multimodal Sentiment Analysis (MSA) leverages multiple data modals to analyze human sentiment. Existing MSA models generally employ cutting-edge multimodal fusion and representation learning-based methods to promote MSA capability. However, there are two key challenges: (i) in existing multimodal fusion methods, the decoupling of modal combinations and tremendous parameter redundancy, lead to insufficient fusion performance and efficiency; (ii) a challenging trade-off exists between representation capability and computational overhead in unimodal feature extractors and encoders. Our proposed G

Research Question

What is the computational overhead and latency trade-off of graph-based fusion methods versus token redundancy reduction techniques in cross-document NLI tasks?

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