A unified evaluation framework testing gradient and loss-based balancing strategies under controlled settings, and a theoretical diagnosis explaining why these methods fail, as they conflate fitting speed with discriminative contribution are provided.
Abstract
Multimodal Sentiment Analysis (MSA) remains constrained by modality imbalance, yet the field continues to rely on optimization-based balancing methods that promise more than they deliver. We provide three contributions: 1) a unified evaluation framework testing gradient and loss-based balancing strategies under controlled settings; 2) a theoretical diagnosis explaining why these methods fail, as they conflate fitting speed with discriminative contribution; and 3) a research agenda toward held-out discriminative modality valuation. Experiments on CMU-MOSI and CMU-MOSEI reveal three shortcomings: no strategy reliably outperforms Late Concatenation; performance is sensitive to hyperparameters; and even ratio calibration fails to yield consistent gains. The core issue is fundamental: loss is not utility, and gradients are not importance. Modality imbalance remains unresolved, motivating utility estimation from held-out performance.
This work introduces an adaptive variational information bottleneck to model modality-wise uncertainty and perform quality-aware information compression, thereby suppressing redundant noise in unreliable modalities and designs a reliability-aware cross-sample enhancement strategy that retrieves high-confidence, semanti...
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Sentiment Analysis (SA) is a fundamental application of Natural Language Processing (NLP), historically relying on a three-way classification (positive, negative, neutral) to evaluate public opinion trends. However, sarcasm poses a critical challenge to these systems due to "polarity inversion," leading to misleading...
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MIT News · Artificial Intelligence· news.mit.eduSep 24, 2026
